GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia

<!DOCTYPE html> <html lang="en" xmlns:mml="http://www.w3.org/1998/Math/MathML"> <head> <meta charset="utf-8"> <meta http-equiv="x-ua-compatible" content="ie=edge"> <title>GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia</title>  <link rel="preload" href="/assets/fonts/montserrat-v15-latin-ext_cyrillic-ext-regular.woff2" as="font" type="font/woff2" crossorigin="anonymous"> <link rel="preload" href="/assets/fonts/montserrat-v25-latin-ext_cyrillic-ext-500.woff2" as="font" type="font/woff2" crossorigin="anonymous"> <link rel="preload" href="/assets/fonts/montserrat-v15-latin-ext_cyrillic-ext-700.woff2" as="font" type="font/woff2" crossorigin="anonymous">  <link rel="preload" href="/assets/fonts/merriweather-v30-latin-ext_cyrillic-ext-300.woff2" as="font" type="font/woff2" crossorigin="anonymous"> <link rel="preload" href="/assets/fonts/merriweather-v22-latin-ext_cyrillic-ext-regular.woff2" as="font" type="font/woff2" crossorigin="anonymous"> <link rel="preload" href="/assets/fonts/merriweather-v22-latin-ext_cyrillic-ext-700.woff2" as="font" type="font/woff2" crossorigin="anonymous">  <link rel="preload" href="/assets/stylesheets/fontawesome.woff2" as="font" type="font/woff2" crossorigin="anonymous"> <link rel="stylesheet" media="all" href='/assets/stylesheets/02f295d1f6a8e9c282e7831cd493e8ff-bootstrap.purged.min.css' /> <link rel="stylesheet" media="all" href='/assets/stylesheets/5337b0511e7530e28f005276f70cde44-main.min.css' /> <link rel="stylesheet" media="all" href='/assets/stylesheets/13761c60ba5201f465803f3005ccd7ef-fontawesome-codes.css' /> <link rel="shortcut icon" type="image/x-icon" href='/assets/images/ec7d7606b4e2f3f921b5e1700948efb6-favicon.ico' /> <link rel="alternate" hreflang="en" href="https://www.degruyter.com/document/doi/10.1515/geo-2019-0056/html?lang=en" /> <link rel="alternate" hreflang="de" href="https://www.degruyter.com/document/doi/10.1515/geo-2019-0056/html?lang=de" /> <link rel="alternate" hreflang="x-default" href="https://www.degruyter.com/document/doi/10.1515/geo-2019-0056/html" />  <link rel="schema.dcterms" href="http://purl.org/dc/terms/"> <meta name="dcterms.rightsHolder" content="Walter de Gruyter GmbH"> <meta name="dcterms.rights" content="De Gruyter expressly reserves the right to use all content for commercial text and data mining within the meaning of Section 44b of the German Copyright Act."> <link rel="dns-prefetch" href="https://www.google-analytics.com" /> <meta name="google" content="notranslate" /> <meta name="viewport" content="width=device-width, initial-scale=1" /> <meta name="description" content="The Tunisian North-western region, especially Tabarka and Ain-Drahim villages, presents many landslides every year. Therefore, the landslide susceptibility mapping is essential to frame zones with high landslide susceptibility, to avoid loss of lives and properties. In this study, two bivariate statistical models: the evidential belief functions (EBF) and the weight of evidence (WoE), were used to produce landslide susceptibility maps for the study area. For this, a landslide inventory map was mapped using aerial photo, satellite image and extensive field survey. A total of 451 landslides were randomly separated into two datasets: 316 landslides (70%) for modelling and 135 landslides (30%) for validation. Then, 11 landslide conditioning factors: elevation, slope, aspect, lithology, rainfall, normalized difference vegetation index (NDVI), land cover/use, plan curvature, profile curvature, distance to faults and distance to drainage networks, were considered for modelling. The EBF and WoE models were well validated using the Area Under the Receiver Operating Characteristic (AUROC) curve with a success rate of 87.9% and 89.5%, respectively, and a predictive rate of 84.8% and 86.5%, respectively. The landslide susceptibility maps were very similar by the two models, but the WoE model is more efficient and it can be useful in future planning for the current study area."/> <meta property="og:url" content="https://www.degruyter.com/document/doi/10.1515/geo-2019-0056/html"/><meta property="og:site_name" content="De Gruyter"/><meta property="og:title" content="GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia"/><meta property="og:type" content="article"/><meta property="og:locale" content="en"/><meta property="og:image" content="https://www.degruyter.com/document/cover/journal_key/GEO/product"/><meta property="og:image:type" content="image/jpeg"/><meta property="og:description" content="The Tunisian North-western region, especially Tabarka and Ain-Drahim villages, presents many landslides every year. Therefore, the landslide susceptibility mapping is essential to frame zones with high landslide susceptibility, to avoid loss of lives and properties. In this study, two bivariate statistical models: the evidential belief functions (EBF) and the weight of evidence (WoE), were used to produce landslide susceptibility maps for the study area. For this, a landslide inventory map was mapped using aerial photo, satellite image and extensive field survey. A total of 451 landslides were randomly separated into two datasets: 316 landslides (70%) for modelling and 135 landslides (30%) for validation. Then, 11 landslide conditioning factors: elevation, slope, aspect, lithology, rainfall, normalized difference vegetation index (NDVI), land cover/use, plan curvature, profile curvature, distance to faults and distance to drainage networks, were considered for modelling. The EBF and WoE models were well validated using the Area Under the Receiver Operating Characteristic (AUROC) curve with a success rate of 87.9% and 89.5%, respectively, and a predictive rate of 84.8% and 86.5%, respectively. The landslide susceptibility maps were very similar by the two models, but the WoE model is more efficient and it can be useful in future planning for the current study area."/><meta property="og:locale:alternate" content="de"/><meta property="article:author" content="Zorgati Anis"/><meta property="article:author" content="Gallala Wissem"/><meta property="article:author" content="Vakhshoori Vali"/><meta property="article:author" content="Habib Smida"/><meta property="article:author" content="Gaied Mohamed Essghaier"/><meta property="article:tag" content="Landslide susceptibility mapping"/><meta property="article:tag" content="bivariate statistical model"/><meta property="article:tag" content="Tunisia"/><meta property="article:tag" content="weight of evidence"/><meta property="article:tag" content="evidential belief functions"/><meta property="article:published_time" content="2019-01-01"/><meta property="article:section" content="Open Geosciences"/> <meta name="citation_firstpage" content="708" /> <meta name="citation_lastpage" content="726" /> <meta name="citation_issue" content="1" /> <meta name="citation_issn" content="2391-5447" /> <meta name="citation_language" content='en' /> <meta name="citation_volume" content="11" /> <meta name="citation_publisher" content='De Gruyter Open Access' /> <meta name="citation_pdf_url" content="https://www.degruyter.com/document/doi/10.1515/geo-2019-0056/pdf" /> <meta name="citation_keywords" content='Landslide susceptibility mapping; bivariate statistical model; Tunisia; weight of evidence; evidential belief functions' /> <meta name="citation_author" content="Zorgati Anis" /> <meta name="citation_author" content="Gallala Wissem" /> <meta name="citation_author" content="Vakhshoori Vali" /> <meta name="citation_author" content="Habib Smida" /> <meta name="citation_author" content="Gaied Mohamed Essghaier" /> <meta name="citation_title" content='GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia' /> <meta name="citation_xml_url" content="https://www.degruyter.com/document/doi/10.1515/geo-2019-0056/xml" /> <meta name="citation_journal_title" content="Open Geosciences" /> <meta name="citation_publication_date" content='2019/01/01' /> <meta name="citation_doi" content="10.1515/geo-2019-0056" /> <meta name="citation_fulltext_world_readable" content="" /> <script type="application/ld+json">{"author":[{"@type":"Person","name":"Zorgati Anis"},{"@type":"Person","name":"Gallala Wissem"},{"@type":"Person","name":"Vakhshoori Vali"},{"@type":"Person","name":"Habib Smida"},{"@type":"Person","name":"Gaied Mohamed Essghaier"}],"editor":[],"audience":null,"datePublished":"2019-01-01","headline":"","keywords":"keyword,keyword,keyword,keyword,keyword","isAccessibleForFree":true,"publisher":{"@type":"Organization","name":"De Gruyter Open Access"},"@context":"https://schema.org","@type":"Article","name":"GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia","image":"https://www.degruyter.com/document/cover/journal_key/GEO/thumbnail","url":"https://doi.org/10.1515/geo-2019-0056"}</script> <script nonce="Vqa6Drp/9Ur1pVH5cNZuuw=="> // Define dataLayer and the gtag function. window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} function getCookieValue(name){ const regex = new RegExp(`(^| )${name}=([^;]+)`) const match = document.cookie.match(regex) if (match) { return match[2] } } // Set default consent to 'denied' as a placeholder // Determine actual values based on your own requirements const acceptCookies = getCookieValue('acceptcookies') === "true"; const defaultSettings = (acceptCookies) => ({ 'ad_storage': acceptCookies ? 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Your documents are now available to view.</p> <div id="orderCompleteItemList"> </div> </div> </div> </div> </div> <main id="main" class='language_en min-vh-100 '> <div class="row no-theme-gutter-x g-0 " id="docContent"> <div class='offset-lg-1 col-lg-8 pb-2'> <div class='row g-0 '> <div class="col p-3 pt-5"> <div class="d-flex flex-wrap flex-column flex-md-row justify-content-between"> <div> <div class="mb-2"> <span> <a class="creative-commons-license ga_creative_commons_license text-decoration-none" href="https://creativecommons.org/licenses/by/4.0/" title='Creative Commons - Some Rights Reserved'> <img src="/assets/images/cc-licenses/by.svg" height="100%" width="100%" alt='BY 4.0 license'/> </a> </span> <span class="fa fas fa-icon-open-access"></span> <span class="accessOpenAccess me-2">Open Access</span> <span class="publisherAndPublicationDate metadataInfoMainContent"> <span class="publisher"> Published by <a class="ga_published_by ga_published_by_header" href='/search?query=*&publisherFacet=De+Gruyter'>De Gruyter Open Access</a> </span> <span class="publicationDate">October 31, 2019</span> </span> </div> <div id="mobilePurchaseDiv" class="d-none"> <a class="mobilePurchaseButton" href="#"> Purchase article <svg width="17" height="16" viewBox="0 0 17 16" fill="none" xmlns="http://www.w3.org/2000/svg"> <path fill-rule="evenodd" clip-rule="evenodd" d="M12.75 10.3238L11.4513 9L9.5 11.1469L9.5 1L7.5 1L7.5 11.1473L5.54829 9L4.24962 10.3238L8.49981 15L12.75 10.3238Z" fill="#007596"/> </svg> </a> </div> <h1>GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia</h1> <ul class="contributors list-unstyled mb-2"> <li class="contributors-AUTHOR mb-2"> <span class="metadataAndContributorsFont"><span class="contributor"> <span class="displayName linkAnimation">Zorgati Anis</span> <contributor-popdown name="Zorgati Anis" position="1" email="zorgati.aniss@gmail.com" affiliations="Universite de Gabes Institut Superieur des Sciences et Techniques des Eaux Gabes Sousse, Sousse, Tunisia" > </contributor-popdown> <a href="mailto:zorgati.aniss@gmail.com"> <img alt="EMAIL logo" src='/assets/images/db2546a9d03b905bae083962a41791e1-mail.svg' width="16" height="12" /> </a> </span><span class="comma">, </span><span class="contributor"> <span class="displayName linkAnimation">Gallala Wissem</span> <contributor-popdown name="Gallala Wissem" position="2" email="" affiliations="Universite de Gabes Institut Superieur des Sciences et Techniques des Eaux Gabes Sousse, Sousse, Tunisia" > </contributor-popdown> </span><span class="comma">, </span><span class="contributor"> <span class="displayName linkAnimation">Vakhshoori Vali</span> <contributor-popdown name="Vakhshoori Vali" position="3" email="" affiliations="Universite de Gabes Institut Superieur des Sciences et Techniques des Eaux Gabes Sousse, Sousse, Tunisia" > </contributor-popdown> </span><span class="comma">, </span><span class="contributor"> <span class="displayName linkAnimation">Habib Smida</span> <contributor-popdown name="Habib Smida" position="4" email="" affiliations="Faculty of Earth Sciences, King Abdulaziz University, Jeddah Saudi Arabia; Laboratory of Water Energy and Envirenment, University of Sfax Tunisia, Jeddah Saudi Arabia" > </contributor-popdown> </span> and <span class="contributor"> <span class="displayName linkAnimation">Gaied Mohamed Essghaier</span> <contributor-popdown name="Gaied Mohamed Essghaier" position="5" email="" affiliations="Universite de Gabes Institut Superieur des Sciences et Techniques des Eaux Gabes Sousse, Sousse, Tunisia" > </contributor-popdown> </span></span> </li> </ul> <div class="subTitleInfoProductPage">From the journal <a class="ga_parent ga_parent_journal" href="/journal/key/geo/html">Open Geosciences</a></div> <div class="doi"><a href="https://doi.org/10.1515/geo-2019-0056" class="linkWithoutStyle subTitleInfoProductPage ga_doi" target="_blank">https://doi.org/10.1515/geo-2019-0056</a></div> </div> <div class="d-sm-none pt-3 pb-3 border-bottom"> <div class="alternateForms d-none"> <a href="/document/doi/10.1515/geo-2019-0056/pdf?licenseType=open-access" data-doi="10.1515/geo-2019-0056" class="ga_download_button_pdf_article downloadCompletePdfArticle downloadPdf btn btn-primary fw-bold py-2 w-100 vgwort-click"> <span>Download article (PDF)</span> <span class="fa fas fa-download-button"></span> </a> </div> </div> </div> </div> <div class="d-flex align-items-center flex-wrap px-4 px-lg-2 "> <button id='citationsModalButton' type="button" class="btn btn-main-content ga_cite_this me-2" href='#citationsModal' data-bs-toggle="modal" data-bs-target="#citationsModal" data-doi="10.1515/geo-2019-0056" aria-controls='citationsModal'> Cite this </button> <button id="socialModalButton" type="button" class="btn btn-main-content ga_share_this me-2" href="#socialModal" data-bs-toggle="modal" data-bs-target="#socialModal" aria-controls="socialModal"> Share this </button> <div class="dimensions __dimensions_badge_embed__ ga_dimensions_citation me-2" data-doi="10.1515/geo-2019-0056" data-hide-zero-citations="true" data-legend="never" data-style="large_rectangle"></div> </div> <div id="div-document-progress-bar" class="sticky-top d-none"> <div class="row"> <div class="col-12"> <div class="progress progress-bar-toolbar"> <div id="document-progress-bar" class="progress-bar bg-primary" role="progressbar" aria-label="Document progress bar" aria-valuenow="0" aria-valuemin="0" aria-valuemax="100"></div> </div> </div> </div> </div> <div id="document-main-content" class='row'> <div class="container-fluid px-3 px-lg-0 py-2"> <div class="col"> <div class="d-none analyticsHolder" data-subjects='GS|GS-05|GS-06' data-publisherCode='DG_OA' data-license='open-access' data-publisher='De Gruyter Open Access' data-contentName='GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia' data-doi='10.1515/geo-2019-0056' data-parentIdentifier='GEO' data-parentName='Open Geosciences' data-languages='en' ></div> <div id="documentContent" class="content py-2" data-doi='10.1515/geo-2019-0056' data-accessrestricted="false" data-countertype="document"> <div class="px-2"> <div id="text-container"> <div xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" xmlns:dgpm="http://degruyter.com/resources/fetched-pubmed-id" class="contentWrapper"><div class="article" lang="en"><div class="abstract"><h2 class="subheading">Abstract</h2><p>The Tunisian North-western region, especially Tabarka and Ain-Drahim villages, presents many landslides every year. Therefore, the landslide susceptibility mapping is essential to frame zones with high landslide susceptibility, to avoid loss of lives and properties. In this study, two bivariate statistical models: the evidential belief functions (EBF) and the weight of evidence (WoE), were used to produce landslide susceptibility maps for the study area. For this, a landslide inventory map was mapped using aerial photo, satellite image and extensive field survey. A total of 451 landslides were randomly separated into two datasets: 316 landslides (70%) for modelling and 135 landslides (30%) for validation. Then, 11 landslide conditioning factors: elevation, slope, aspect, lithology, rainfall, normalized difference vegetation index (NDVI), land cover/use, plan curvature, profile curvature, distance to faults and distance to drainage networks, were considered for modelling. The EBF and WoE models were well validated using the Area Under the Receiver Operating Characteristic (AUROC) curve with a success rate of 87.9% and 89.5%, respectively, and a predictive rate of 84.8% and 86.5%, respectively. The landslide susceptibility maps were very similar by the two models, but the WoE model is more efficient and it can be useful in future planning for the current study area.</p></div><div class="keywords mb-3">Keywords: <a href="/search?query=keywordValues%3A%28%22Landslide%20susceptibility%20mapping%22%29%20AND%20journalKey%3A%28%22GEO%22%29&documentVisibility=all&documentTypeFacet=article" class="ga_keyword">Landslide susceptibility mapping</a>; <a href="/search?query=keywordValues%3A%28%22bivariate%20statistical%20model%22%29%20AND%20journalKey%3A%28%22GEO%22%29&documentVisibility=all&documentTypeFacet=article" class="ga_keyword">bivariate statistical model</a>; <a href="/search?query=keywordValues%3A%28%22Tunisia%22%29%20AND%20journalKey%3A%28%22GEO%22%29&documentVisibility=all&documentTypeFacet=article" class="ga_keyword">Tunisia</a>; <a href="/search?query=keywordValues%3A%28%22weight%20of%20evidence%22%29%20AND%20journalKey%3A%28%22GEO%22%29&documentVisibility=all&documentTypeFacet=article" class="ga_keyword">weight of evidence</a>; <a href="/search?query=keywordValues%3A%28%22evidential%20belief%20functions%22%29%20AND%20journalKey%3A%28%22GEO%22%29&documentVisibility=all&documentTypeFacet=article" class="ga_keyword">evidential belief functions</a></div><div class="body"><section id="j_geo-2019-0056_s_001_w2aab3b8d108b1b7b1ab1aAa"><h2 class="subheading">1 Introduction</h2><p>Landslides are considered among the most dangerous natural hazards due to their effect on human’s lives and properties [<a href="#j_geo-2019-0056_ref_001_w2aab3b8d108b1b7b1ab2ab1Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_001_w2aab3b8d108b1b7b1ab2ab1Aa" data-bs-toggle="tooltip" title="[1] Del Ventisette C, Garfagnoli F, Ciampalini A, et al. Catastrophic debris-flows: geological hazard and human influence. EGU General Assembly Conference Abstracts; 2013.10.5194/nhess-12-2907-2012Search in Google Scholar">1</a>, <a href="#j_geo-2019-0056_ref_002_w2aab3b8d108b1b7b1ab2ab2Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_002_w2aab3b8d108b1b7b1ab2ab2Aa" data-bs-toggle="tooltip" title="[2] Aleotti P, Chowdhury R. Landslide hazard assessment: summary review and new perspectives. Bulletin of Engineering Geology and the environment 1999;58:21-44.10.1007/s100640050066Search in Google Scholar">2</a>]. The north western area of Tunisia presents many landslides every year. For example in February 2012, Ain-Drahim village was isolated due to roads destruction by landslides. In addition, 98 people were directly affected and 7 deaths were reported according to National Database of Disaster Losses [<a href="#j_geo-2019-0056_ref_003_w2aab3b8d108b1b7b1ab2ab3Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_003_w2aab3b8d108b1b7b1ab2ab3Aa" data-bs-toggle="tooltip" title="[3] NDDL. https://unictunis.org.tn/category/catastrophes-naturelles/ 2012.Search in Google Scholar">3</a>]. Despite the importance of landslide study and zonation in the Tunisian North western area, there are no studies done in the region.</p><p>Landslides are controlled by several natural conditioning factors such as: slope, rainfall, lithology, tectonics, etc. [<a href="#j_geo-2019-0056_ref_002_w2aab3b8d108b1b7b1ab2ab2Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_002_w2aab3b8d108b1b7b1ab2ab2Aa" data-bs-toggle="tooltip" title="[2] Aleotti P, Chowdhury R. Landslide hazard assessment: summary review and new perspectives. Bulletin of Engineering Geology and the environment 1999;58:21-44.10.1007/s100640050066Search in Google Scholar">2</a>, <a href="#j_geo-2019-0056_ref_004_w2aab3b8d108b1b7b1ab2ab4Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_004_w2aab3b8d108b1b7b1ab2ab4Aa" data-bs-toggle="tooltip" title="[4] Ayalew L, Yamagishi H. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 2005;65:15-31.10.1016/j.geomorph.2004.06.010Search in Google Scholar">4</a>]. So, in nature there are areas which are more prone to landslides than others whereby landslide susceptibility is defined as the spatial distribution or probability of the occurrence of landslides [<a href="#j_geo-2019-0056_ref_005_w2aab3b8d108b1b7b1ab2ab5Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_005_w2aab3b8d108b1b7b1ab2ab5Aa" data-bs-toggle="tooltip" title="[5] Constantin M, Bednarik M, Jurchescu MC, Vlaicu M. 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Probabilistic landslide hazard assessment at the basin scale. Geomorphology 2005;72:272-99.10.1016/j.geomorph.2005.06.002Search in Google Scholar">7</a>]. The damage of landslides could be significantly decreased by establishing landslide susceptibility maps [<a href="#j_geo-2019-0056_ref_008_w2aab3b8d108b1b7b1ab2ab8Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_008_w2aab3b8d108b1b7b1ab2ab8Aa" data-bs-toggle="tooltip" title="[8] Pradhan B. Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. Journal of the Indian Society of Remote Sensing 2010;38:301-20.10.1007/s12524-010-0020-zSearch in Google Scholar">8</a>].</p><p>The assessment of slope stability is carried out using two approaches [<a href="#j_geo-2019-0056_ref_009_w2aab3b8d108b1b7b1ab2ab9Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_009_w2aab3b8d108b1b7b1ab2ab9Aa" data-bs-toggle="tooltip" title="[9] Guzzetti F, Carrara A, Cardinali M, Reichenbach P. Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 1999;31:181-216.10.1016/S0169-555X(99)00078-1Search in Google Scholar">9</a>, <a href="#j_geo-2019-0056_ref_010_w2aab3b8d108b1b7b1ab2ac10Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_010_w2aab3b8d108b1b7b1ab2ac10Aa" data-bs-toggle="tooltip" title="[10] Soeters R, Van Westen CJ. Landslides: Investigation and mitigation. Chapter 8-Slope instability recognition, analysis, and zonation. Transportation research board special report 1996.Search in Google Scholar">10</a>, <a href="#j_geo-2019-0056_ref_011_w2aab3b8d108b1b7b1ab2ac11Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_011_w2aab3b8d108b1b7b1ab2ac11Aa" data-bs-toggle="tooltip" title="[11] Thiery Y, Malet J-P, Sterlacchini S, Puissant A, Maquaire O. Analyse spatiale de la susceptibilité des versants aux glissements de terrain. Revue Internationale de Géomatique 2005;15:227-45.10.3166/rig.15.227-245Search in Google Scholar">11</a>]: the direct or qualitative method based on expert knowledge [<a href="#j_geo-2019-0056_ref_012_w2aab3b8d108b1b7b1ab2ac12Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_012_w2aab3b8d108b1b7b1ab2ac12Aa" data-bs-toggle="tooltip" title="[12] Van Westen CJ. The modelling of landslide hazards using GIS. Surveys in Geophysics 2000;21:241-55.10.1023/A:1006794127521Search in Google Scholar">12</a>] and the indirect or quantitative method based on statistical algorithms [<a href="#j_geo-2019-0056_ref_002_w2aab3b8d108b1b7b1ab2ab2Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_002_w2aab3b8d108b1b7b1ab2ab2Aa" data-bs-toggle="tooltip" title="[2] Aleotti P, Chowdhury R. Landslide hazard assessment: summary review and new perspectives. Bulletin of Engineering Geology and the environment 1999;58:21-44.10.1007/s100640050066Search in Google Scholar">2</a>, <a href="#j_geo-2019-0056_ref_010_w2aab3b8d108b1b7b1ab2ac10Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_010_w2aab3b8d108b1b7b1ab2ac10Aa" data-bs-toggle="tooltip" title="[10] Soeters R, Van Westen CJ. Landslides: Investigation and mitigation. Chapter 8-Slope instability recognition, analysis, and zonation. Transportation research board special report 1996.Search in Google Scholar">10</a>, <a href="#j_geo-2019-0056_ref_013_w2aab3b8d108b1b7b1ab2ac13Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_013_w2aab3b8d108b1b7b1ab2ac13Aa" data-bs-toggle="tooltip" title="[13] Bourenane H, Bouhadad Y, Guettouche MS, Braham M. GIS-based landslide susceptibility zonation using bivariate statistical and expert approaches in the city of Constantine (Northeast Algeria). Bulletin of Engineering Geology and the Environment 2015;74:337-55.10.1007/s10064-014-0616-6Search in Google Scholar">13</a>]. The direct or qualitative method such as analytical hierarchy process (AHP) was used in landslide susceptibility mapping [<a href="#j_geo-2019-0056_ref_014_w2aab3b8d108b1b7b1ab2ac14Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_014_w2aab3b8d108b1b7b1ab2ac14Aa" data-bs-toggle="tooltip" title="[14] Chen W, Li W, Chai H, Hou E, Li X, Ding X. GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China. Environmental Earth Sciences 2016;75:63.10.1007/s12665-015-4795-7Search in Google Scholar">14</a>, <a href="#j_geo-2019-0056_ref_015_w2aab3b8d108b1b7b1ab2ac15Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_015_w2aab3b8d108b1b7b1ab2ac15Aa" data-bs-toggle="tooltip" title="[15] Hadji R, Achour Y, Hamed Y. Using GIS and RS for Slope Movement SusceptibilityMapping: Comparing AHP, LI and LR Methods for the Oued Mellah Basin, NE Algeria. Euro-Mediterranean Conference for Environmental Integration; 2017: Springer. p. 1853-6.10.1007/978-3-319-70548-4_536Search in Google Scholar">15</a>, <a href="#j_geo-2019-0056_ref_016_w2aab3b8d108b1b7b1ab2ac16Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_016_w2aab3b8d108b1b7b1ab2ac16Aa" data-bs-toggle="tooltip" title="[16] Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA. Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria. Arabian Journal of Geo-sciences 2017;10:194.10.1007/s12517-017-2980-6Search in Google Scholar">16</a>]. The indirect or quantitative method was widely used in the literature as the artificial neural network (ANN) [<a href="#j_geo-2019-0056_ref_017_w2aab3b8d108b1b7b1ab2ac17Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_017_w2aab3b8d108b1b7b1ab2ac17Aa" data-bs-toggle="tooltip" title="[17] Pradhan B, Lee S. Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Sciences 2010;60:1037-54.10.1007/s12665-009-0245-8Search in Google Scholar">17</a>, <a href="#j_geo-2019-0056_ref_018_w2aab3b8d108b1b7b1ab2ac18Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_018_w2aab3b8d108b1b7b1ab2ac18Aa" data-bs-toggle="tooltip" title="[18] Nourani V, Pradhan B, Ghaffari H, Sharifi SS. Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models. Natural hazards 2014;71:523-47.10.1007/s11069-013-0932-3Search in Google Scholar">18</a>, <a href="#j_geo-2019-0056_ref_019_w2aab3b8d108b1b7b1ab2ac19Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_019_w2aab3b8d108b1b7b1ab2ac19Aa" data-bs-toggle="tooltip" title="[19] Zare M, Pourghasemi HR, Vafakhah M, Pradhan B. Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arabian Journal of Geosciences 2013;6:2873-88.10.1007/s12517-012-0610-xSearch in Google Scholar">19</a>], support vector machine (SVM) [<a href="#j_geo-2019-0056_ref_020_w2aab3b8d108b1b7b1ab2ac20Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_020_w2aab3b8d108b1b7b1ab2ac20Aa" data-bs-toggle="tooltip" title="[20] Peng L, Niu R, Huang B, Wu X, Zhao Y, Ye R. Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China. 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Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Computers & Geosciences 2012;45:199-211.10.1016/j.cageo.2011.10.031Search in Google Scholar">22</a>, <a href="#j_geo-2019-0056_ref_023_w2aab3b8d108b1b7b1ab2ac23Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_023_w2aab3b8d108b1b7b1ab2ac23Aa" data-bs-toggle="tooltip" title="[23] Sdao F, Lioi D, Pascale S, Caniani D, Mancini I. Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera. Natural hazards and earth system sciences 2013;13:395.10.5194/nhess-13-395-2013Search in Google Scholar">23</a>]. Also the bivariate statistical methods as an indirect methods were used by many researchers like the certainty factor (CF) [<a href="#j_geo-2019-0056_ref_006_w2aab3b8d108b1b7b1ab2ab6Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_006_w2aab3b8d108b1b7b1ab2ab6Aa" data-bs-toggle="tooltip" title="[6] Cui K, Lu D, Li W.Comparison of landslide susceptibility mappingbased on statistical index, certainty factors, weights of evidence and evidential belief function models. Geocarto International 2017;32:935-55.10.1080/10106049.2016.1195886Search in Google Scholar">6</a>, <a href="#j_geo-2019-0056_ref_024_w2aab3b8d108b1b7b1ab2ac24Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_024_w2aab3b8d108b1b7b1ab2ac24Aa" data-bs-toggle="tooltip" title="[24] Liu M, Chen X, Yang S. Collapse landslide and mudslides hazard zonation. Landslide science for a safer geoenvironment: Springer; 2014:457-62.10.1007/978-3-319-05050-8_71Search in Google Scholar">24</a>, <a href="#j_geo-2019-0056_ref_025_w2aab3b8d108b1b7b1ab2ac25Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_025_w2aab3b8d108b1b7b1ab2ac25Aa" data-bs-toggle="tooltip" title="[25] Ilia I, Koumantakis I, Rozos D, Koukis G, Tsangaratos P. A geographical information system (GIS) based probabilistic certainty factor approach in assessing landslide susceptibility: the case study of Kimi, Euboea, Greece. Engineering Geology for Society and Territory-Volume 2: Springer; 2015:1199-204.10.1007/978-3-319-09057-3_210Search in Google Scholar">25</a>], statistical index (SI) [<a href="#j_geo-2019-0056_ref_006_w2aab3b8d108b1b7b1ab2ab6Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_006_w2aab3b8d108b1b7b1ab2ab6Aa" data-bs-toggle="tooltip" title="[6] Cui K, Lu D, Li W.Comparison of landslide susceptibility mappingbased on statistical index, certainty factors, weights of evidence and evidential belief function models. Geocarto International 2017;32:935-55.10.1080/10106049.2016.1195886Search in Google Scholar">6</a>, <a href="#j_geo-2019-0056_ref_026_w2aab3b8d108b1b7b1ab2ac26Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_026_w2aab3b8d108b1b7b1ab2ac26Aa" data-bs-toggle="tooltip" title="[26] Regmi AD, Devkota KC, Yoshida K, et al. Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arabian Journal of Geosciences 2014;7:725-42.10.1007/s12517-012-0807-zSearch in Google Scholar">26</a>, <a href="#j_geo-2019-0056_ref_027_w2aab3b8d108b1b7b1ab2ac27Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_027_w2aab3b8d108b1b7b1ab2ac27Aa" data-bs-toggle="tooltip" title="[27] Kavzoglu T, Sahin EK, Colkesen I. An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district. Natural Hazards 2015;76:471-96.10.1007/s11069-014-1506-8Search in Google Scholar">27</a>], frequency ratio (FR) [<a href="#j_geo-2019-0056_ref_008_w2aab3b8d108b1b7b1ab2ab8Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_008_w2aab3b8d108b1b7b1ab2ab8Aa" data-bs-toggle="tooltip" title="[8] Pradhan B. Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. Journal of the Indian Society of Remote Sensing 2010;38:301-20.10.1007/s12524-010-0020-zSearch in Google Scholar">8</a>, <a href="#j_geo-2019-0056_ref_028_w2aab3b8d108b1b7b1ab2ac28Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_028_w2aab3b8d108b1b7b1ab2ac28Aa" data-bs-toggle="tooltip" title="[28] Vakhshoori V, Zare M. Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods. Geomatics, Natural Hazards and Risk 2016;7:1731-52.10.1080/19475705.2016.1144655Search in Google Scholar">28</a>, <a href="#j_geo-2019-0056_ref_029_w2aab3b8d108b1b7b1ab2ac29Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_029_w2aab3b8d108b1b7b1ab2ac29Aa" data-bs-toggle="tooltip" title="[29] Youssef AM, Al-Kathery M, Pradhan B. Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosciences Journal 2015;19:113-34.10.1007/s12303-014-0032-8Search in Google Scholar">29</a>], evidential belief function (EBF) [<a href="#j_geo-2019-0056_ref_030_w2aab3b8d108b1b7b1ab2ac30Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_030_w2aab3b8d108b1b7b1ab2ac30Aa" data-bs-toggle="tooltip" title="[30] Pradhan B, Abokharima MH, Jebur MN, Tehrany MS. Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Natural hazards 2014;73:1019-42.10.1007/s11069-014-1128-1Search in Google Scholar">30</a>, <a href="#j_geo-2019-0056_ref_031_w2aab3b8d108b1b7b1ab2ac31Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_031_w2aab3b8d108b1b7b1ab2ac31Aa" data-bs-toggle="tooltip" title="[31] Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB. Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the eflcacy of evidential belief functions and fuzzy logic models. Catena 2012;96:28-40.10.1016/j.catena.2012.04.001Search in Google Scholar">31</a>, <a href="#j_geo-2019-0056_ref_032_w2aab3b8d108b1b7b1ab2ac32Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_032_w2aab3b8d108b1b7b1ab2ac32Aa" data-bs-toggle="tooltip" title="[32] Zhang Z, Yang F, Chen H, et al. GIS-based landslide susceptibility analysis using frequency ratio and evidential belief function models. 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Arabian Journal of Geosciences 2014;7:725-42.10.1007/s12517-012-0807-zSearch in Google Scholar">26</a>, <a href="#j_geo-2019-0056_ref_033_w2aab3b8d108b1b7b1ab2ac33Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_033_w2aab3b8d108b1b7b1ab2ac33Aa" data-bs-toggle="tooltip" title="[33] Xu C, Xu X, Dai F, Xiao J, Tan X, Yuan R. Landslide hazard mapping using GIS and weight of evidence model in Qingshui river watershed of 2008 Wenchuan earthquake struck region. Journal of Earth Science 2012;23:97-120.10.1007/s12583-012-0236-7Search in Google Scholar">33</a>]. Also, the multivariate method as the logistic regression was applied in several works [<a href="#j_geo-2019-0056_ref_034_w2aab3b8d108b1b7b1ab2ac34Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_034_w2aab3b8d108b1b7b1ab2ac34Aa" data-bs-toggle="tooltip" title="[34] Lepore C, Kamal SA, Shanahan P, Bras RL. Rainfall-induced landslide susceptibility zonation of Puerto Rico. 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TXT-tool 1.081-6.1 A Comparative Study of the Binary Logistic Regression (BLR) and Artificial Neural Network (ANN) Models for GIS-Based Spatial Predicting Landslides at a Regional Scale. Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools: Springer; 2018:139-51.10.1007/978-3-319-57774-6_10Search in Google Scholar">36</a>].</p><p>The landslide susceptibility mapping statistical methods were widely compared in the literature in different geological, climatologic, geomorphologic, etc. conditions [<a href="#j_geo-2019-0056_ref_006_w2aab3b8d108b1b7b1ab2ab6Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_006_w2aab3b8d108b1b7b1ab2ab6Aa" data-bs-toggle="tooltip" title="[6] Cui K, Lu D, Li W.Comparison of landslide susceptibility mappingbased on statistical index, certainty factors, weights of evidence and evidential belief function models. Geocarto International 2017;32:935-55.10.1080/10106049.2016.1195886Search in Google Scholar">6</a>, <a href="#j_geo-2019-0056_ref_032_w2aab3b8d108b1b7b1ab2ac32Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_032_w2aab3b8d108b1b7b1ab2ac32Aa" data-bs-toggle="tooltip" title="[32] Zhang Z, Yang F, Chen H, et al. GIS-based landslide susceptibility analysis using frequency ratio and evidential belief function models. Environmental Earth Sciences 2016;75:948.10.1007/s12665-016-5732-0Search in Google Scholar">32</a>, <a href="#j_geo-2019-0056_ref_037_w2aab3b8d108b1b7b1ab2ac37Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_037_w2aab3b8d108b1b7b1ab2ac37Aa" data-bs-toggle="tooltip" title="[37] Hong H, Naghibi SA, Pourghasemi HR, Pradhan B. GIS-based landslide spatial modeling in Ganzhou City, China. 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Also, Park [<a href="#j_geo-2019-0056_ref_039_w2aab3b8d108b1b7b1ab2ac39Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_039_w2aab3b8d108b1b7b1ab2ac39Aa" data-bs-toggle="tooltip" title="[39] Park S, Choi C, Kim B, Kim J. Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environmental earth sciences 2013;68:1443-64.10.1007/s12665-012-1842-5Search in Google Scholar">39</a>] noticed an insignificant difference in the landslide susceptibility maps (LSMs) produced using FR, AHP, ANN and LR methods.</p><p>The main aim of this study is the establishment of landslide susceptibility maps of the current study area using two bivariate statistical methods: the evidential belief function (EBF) and weight of evidence (WoE).</p></section><section id="j_geo-2019-0056_s_002_w2aab3b8d108b1b7b1ab1b1Aa"><h2 class="subheading">2 Study area</h2><p>The study area covering 860 km<sup>2</sup>, is located in the North-West of Tunisia, between Ain-Drahim and Tabarka villages, which extends from longitude 8<sup>∘</sup> 25’ 29” E to 8<sup>∘</sup> 59’ 53” E and from latitude 36<sup>∘</sup> 40’ 26” N to 37<sup>∘</sup> 00’ 36” N (<a href="#j_geo-2019-0056_fig_001_w2aab3b8d108b1b7b1ab1b1b3Aa" class="link link-fig" data-bs-target="j_geo-2019-0056_fig_001_w2aab3b8d108b1b7b1ab1b1b3Aa">Figure 1</a>). This zone is situated at an altitude ranging between 3 and 1000m above msl in a mountainous area. The lithological units of the area are mainly composed by the Numidian flyschoidal deposits of Oligocene, lower-Miocene age, essentially consisting of turbiditic sandy and clayey formation [<a href="#j_geo-2019-0056_ref_040_w2aab3b8d108b1b7b1ab2ac40Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_040_w2aab3b8d108b1b7b1ab2ac40Aa" data-bs-toggle="tooltip" title="[40] Glaçon G, Rouvier H. Précisions lithologiques et stratigraphiques sur le “Numidien” de Kroumirie (Tunisie septentrionale). Bull Soc géol France 1967;9:410-7.10.2113/gssgfbull.S7-IX.3.410Search in Google Scholar">40</a>, <a href="#j_geo-2019-0056_ref_041_w2aab3b8d108b1b7b1ab2ac41Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_041_w2aab3b8d108b1b7b1ab2ac41Aa" data-bs-toggle="tooltip" title="[41] Bagga O, Abdeljaouad D, Mercier E. La «zone des nappes» de Tunisie: une marge méso-cénozoïque en blocs basculésmodérément inversée (région de Taberka/Jendouba; Tunisie nord-occidentale). Bulletin Société Géologique de France 2006;177:145-54.Search in Google Scholar">41</a>, <a href="#j_geo-2019-0056_ref_042_w2aab3b8d108b1b7b1ab2ac42Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_042_w2aab3b8d108b1b7b1ab2ac42Aa" data-bs-toggle="tooltip" title="[42] MarzouguiW, Melki F, Arfaoui M, Houla Y, Zargouni F.Major faults, salt structures and paleo-ridge at tectonic nodes in Northern Tunisia: contribution of tectonics and gravity analysis. Arabian Journal of Geosciences 2015;8:7601-17.10.1007/s12517-014-1698-ySearch in Google Scholar">42</a>, <a href="#j_geo-2019-0056_ref_043_w2aab3b8d108b1b7b1ab2ac43Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_043_w2aab3b8d108b1b7b1ab2ac43Aa" data-bs-toggle="tooltip" title="[43] Belayouni H, Guerrera F, Martín MM, Serrano F. Stratigraphic update of the Cenozoic Sub-Numidian formations of the Tunisian Tell (North Africa): Tectonic/sedimentary evolution and correlations along the Maghrebian Chain. Journal of African Earth Sciences 2012;64:48-64.10.1016/j.jafrearsci.2011.11.010Search in Google Scholar">43</a>, <a href="#j_geo-2019-0056_ref_044_w2aab3b8d108b1b7b1ab2ac44Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_044_w2aab3b8d108b1b7b1ab2ac44Aa" data-bs-toggle="tooltip" title="[44] Rouvier H. Géologie de l’Extrême Nord-Tunisien [thesis]: Paris. France, Université Pierre et Marie Curie 1977.Search in Google Scholar">44</a>]. The flysch formations present large changes in structural style [<a href="#j_geo-2019-0056_ref_045_w2aab3b8d108b1b7b1ab2ac45Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_045_w2aab3b8d108b1b7b1ab2ac45Aa" data-bs-toggle="tooltip" title="[45] Luján M, Storti F, Balanyá J-C, Crespo-Blanc A, Rossetti F. Role of décollement material with different rheological properties in the structure of the Aljibe thrust imbricate (Flysch Trough, Gibraltar Arc): an analogue modelling approach. Journal of Structural Geology 2003;25:867-82.10.1016/S0191-8141(02)00087-1Search in Google Scholar">45</a>] and are heterogeneous rock masses which lead to the alteration of hard rock layers (sandstone and siltstones) and weak ones (marls and clay). Also, flysch rocks are influenced by weathering processes which cause changes in strength properties and increases the content of the clay fraction in the weathered zone by alteration of silicate minerals in clay, silt, sand and sandstone [<a href="#j_geo-2019-0056_ref_046_w2aab3b8d108b1b7b1ab2ac46Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_046_w2aab3b8d108b1b7b1ab2ac46Aa" data-bs-toggle="tooltip" title="[46] Attewell P, Farmer I, Glossop N. Ground deformation caused by tunnelling in a silty alluvial clay. Ground Engineering 1978;11.Search in Google Scholar">46</a>] which make flysch rocks more prone to landslide.</p><div class="figure-wrapper" id="j_geo-2019-0056_fig_001_w2aab3b8d108b1b7b1ab1b1b3Aa"><div class="figure w-100"><div class="graphic"><img loading="lazy" src="/document/doi/10.1515/geo-2019-0056/asset/graphic/j_geo-2019-0056_fig_001.jpg" alt="Figure 1 Study area location with landslide inventory."></img></div><div class="figure-description mb-3"><div class="figure-label h3"><span class="label">Figure 1</span></div><div class="figure-caption mb-2"><span class="caption"><p>Study area location with landslide inventory.</p></span></div></div></div></div><p>The climate of the study area is considered Mediterranean, with rainy winters and warm summers. The precipitation ranges from 630 mm (1993) to 2400 mm (2003) with a yearly average precipitation above 1000mm according to the National Institute of Meteorology [<a href="#j_geo-2019-0056_ref_047_w2aab3b8d108b1b7b1ab2ac47Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_047_w2aab3b8d108b1b7b1ab2ac47Aa" data-bs-toggle="tooltip" title="[47] NIM. http://www.meteo.tn/htmlen/accueil.php.Search in Google Scholar">47</a>].</p></section><section id="j_geo-2019-0056_s_003_w2aab3b8d108b1b7b1ab1b2Aa"><h2 class="subheading">3 Data preparation</h2><section id="j_geo-2019-0056_s_003_s_001_w2aab3b8d108b1b7b1ab1b2b2Aa"><h3 class="subheading">3.1 Landslide inventory map</h3><p>A landslide inventory map is crucial for landslide susceptibility mapping [<a href="#j_geo-2019-0056_ref_048_w2aab3b8d108b1b7b1ab2ac48Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_048_w2aab3b8d108b1b7b1ab2ac48Aa" data-bs-toggle="tooltip" title="[48] Van Westen CJ, Castellanos E, Kuriakose SL. Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Engineering geology 2008;102:112-31.10.1016/j.enggeo.2008.03.010Search in Google Scholar">48</a>, <a href="#j_geo-2019-0056_ref_049_w2aab3b8d108b1b7b1ab2ac49Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_049_w2aab3b8d108b1b7b1ab2ac49Aa" data-bs-toggle="tooltip" title="[49] Corominas J, van Westen C, Frattini P, et al. Recommendations for the quantitative analysis of landslide risk. Bulletin of engineering geology and the environment 2014;73:209-63.10.1007/s10064-013-0538-8Search in Google Scholar">49</a>]. This map is the base for the landslide occurrence probability calculation by defining the relationship between landslide occurrences and factors related to them in the past [<a href="#j_geo-2019-0056_ref_050_w2aab3b8d108b1b7b1ab2ac50Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_050_w2aab3b8d108b1b7b1ab2ac50Aa" data-bs-toggle="tooltip" title="[50] Mohammady M, Pourghasemi HR, Pradhan B. Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models. Journal of Asian Earth Sciences 2012;61:221-36.10.1016/j.jseaes.2012.10.005Search in Google Scholar">50</a>, <a href="#j_geo-2019-0056_ref_051_w2aab3b8d108b1b7b1ab2ac51Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_051_w2aab3b8d108b1b7b1ab2ac51Aa" data-bs-toggle="tooltip" title="[51] Park N-W. Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis. Environmental Earth Sciences 2011;62:367-76.10.1007/s12665-010-0531-5Search in Google Scholar">51</a>, <a href="#j_geo-2019-0056_ref_052_w2aab3b8d108b1b7b1ab2ac52Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_052_w2aab3b8d108b1b7b1ab2ac52Aa" data-bs-toggle="tooltip" title="[52] Yalcin A, Reis S, Aydinoglu A, Yomralioglu T. A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 2011;85:274-87.10.1016/j.catena.2011.01.014Search in Google Scholar">52</a>].</p><p>The landslide inventory map of the studied area was produced by aerial photo interpretation with large scale field surveys. Only rotational, transitional and compound landslides were taken into account due to their similar conditioning factors [<a href="#j_geo-2019-0056_ref_028_w2aab3b8d108b1b7b1ab2ac28Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_028_w2aab3b8d108b1b7b1ab2ac28Aa" data-bs-toggle="tooltip" title="[28] Vakhshoori V, Zare M. Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods. Geomatics, Natural Hazards and Risk 2016;7:1731-52.10.1080/19475705.2016.1144655Search in Google Scholar">28</a>]. Thus, 451 landslides were identified in the study area and mapped as polygons. They were randomly subdivided into two data sets: 70% (316 landslides) for the susceptibility model building and 30% (135 landslides) for model validation (<a href="#j_geo-2019-0056_fig_001_w2aab3b8d108b1b7b1ab1b1b3Aa" class="link link-fig" data-bs-target="j_geo-2019-0056_fig_001_w2aab3b8d108b1b7b1ab1b1b3Aa">Figure 1</a>).</p></section><section id="j_geo-2019-0056_s_003_s_002_w2aab3b8d108b1b7b1ab1b2b3Aa"><h3 class="subheading">3.2 Preparation of landslide conditioning factors</h3><p>For this study 11 factors which are: elevation, lithology, slope angle, slope aspect, plan curvature, profile curvature, distance to drainage network, distance to fault, rainfall, NDVI and land use/cover were prepared in ARCGIS 10.4 database as landslide conditioning factors.</p></section><section id="j_geo-2019-0056_s_003_s_003_w2aab3b8d108b1b7b1ab1b2b4Aa"><h3 class="subheading">3.3 Elevation</h3><p>Altitude is considered as a landslide factor in many research papers [<a href="#j_geo-2019-0056_ref_053_w2aab3b8d108b1b7b1ab2ac53Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_053_w2aab3b8d108b1b7b1ab2ac53Aa" data-bs-toggle="tooltip" title="[53] Gruber S, Haeberli W. Permafrost in steep bedrock slopes and its temperature-related destabilization following climate change. Journal of Geophysical Research: Earth Surface 2007;112.10.1029/2006JF000547Search in Google Scholar">53</a>, <a href="#j_geo-2019-0056_ref_054_w2aab3b8d108b1b7b1ab2ac54Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_054_w2aab3b8d108b1b7b1ab2ac54Aa" data-bs-toggle="tooltip" title="[54] Rozos D, Pyrgiotis L, Skias S, Tsagaratos P. An implementation of rock engineering system for ranking the instability potential of natural slopes in Greek territory. An application in Karditsa County. Landslides 2008;5:261-70.10.1007/s10346-008-0117-4Search in Google Scholar">54</a>]. In this study, a DEM with 30 x 30 grid size was used and reclassified into five classes with a 200m interval: <200, 200-400, 400-600, 600-800 and >800 (<a href="#j_geo-2019-0056_fig_003_w2aab3b8d108b1b7b1ab1b2b4b4Aa" class="link link-fig" data-bs-target="j_geo-2019-0056_fig_003_w2aab3b8d108b1b7b1ab1b2b4b4Aa">Figure 2b).</a></p><div class="figure-wrapper" id="j_geo-2019-0056_fig_002_w2aab3b8d108b1b7b1ab1b2b4b3Aa"><div class="figure w-100"><div class="graphic"><img loading="lazy" src="/document/doi/10.1515/geo-2019-0056/asset/graphic/j_geo-2019-0056_fig_002.jpg" alt="Figure 2a Landslide conditioning factors – slope aspect"></img></div><div class="figure-description mb-3"><div class="figure-label h3"><span class="label">Figure 2a</span></div><div class="figure-caption mb-2"><span class="caption"><p>Landslide conditioning factors – slope aspect</p></span></div></div></div></div><div class="figure-wrapper" id="j_geo-2019-0056_fig_003_w2aab3b8d108b1b7b1ab1b2b4b4Aa"><div class="figure w-100"><div class="graphic"><img loading="lazy" src="/document/doi/10.1515/geo-2019-0056/asset/graphic/j_geo-2019-0056_fig_003.jpg" alt="Figure 2b Landslide conditioning factors – elevation"></img></div><div class="figure-description mb-3"><div class="figure-label h3"><span class="label">Figure 2b</span></div><div class="figure-caption mb-2"><span class="caption"><p>Landslide conditioning factors – elevation</p></span></div></div></div></div><div class="figure-wrapper" id="j_geo-2019-0056_fig_004_w2aab3b8d108b1b7b1ab1b2b4b5Aa"><div class="figure w-100"><div class="graphic"><img loading="lazy" src="/document/doi/10.1515/geo-2019-0056/asset/graphic/j_geo-2019-0056_fig_004.jpg" alt="Figure 2c Landslide conditioning factors – distance to fault"></img></div><div class="figure-description mb-3"><div class="figure-label h3"><span class="label">Figure 2c</span></div><div class="figure-caption mb-2"><span class="caption"><p>Landslide conditioning factors – distance to fault</p></span></div></div></div></div><div class="figure-wrapper" id="j_geo-2019-0056_fig_005_w2aab3b8d108b1b7b1ab1b2b4b6Aa"><div class="figure w-100"><div class="graphic"><img loading="lazy" src="/document/doi/10.1515/geo-2019-0056/asset/graphic/j_geo-2019-0056_fig_005.jpg" alt="Figure 2d Landslide conditioning factors – land cover/use"></img></div><div class="figure-description mb-3"><div class="figure-label h3"><span class="label">Figure 2d</span></div><div class="figure-caption mb-2"><span class="caption"><p>Landslide conditioning factors – land cover/use</p></span></div></div></div></div><div class="figure-wrapper" id="j_geo-2019-0056_fig_006_w2aab3b8d108b1b7b1ab1b2b4b7Aa"><div class="figure w-100"><div class="graphic"><img loading="lazy" src="/document/doi/10.1515/geo-2019-0056/asset/graphic/j_geo-2019-0056_fig_006.jpg" alt="Figure 2e Landslide conditioning factors – lithology"></img></div><div class="figure-description mb-3"><div class="figure-label h3"><span class="label">Figure 2e</span></div><div class="figure-caption mb-2"><span class="caption"><p>Landslide conditioning factors – lithology</p></span></div></div></div></div><div class="figure-wrapper" id="j_geo-2019-0056_fig_007_w2aab3b8d108b1b7b1ab1b2b4b8Aa"><div class="figure w-100"><div class="graphic"><img loading="lazy" src="/document/doi/10.1515/geo-2019-0056/asset/graphic/j_geo-2019-0056_fig_007.jpg" alt="Figure 2f Landslide conditioning factors – NDVI"></img></div><div class="figure-description mb-3"><div class="figure-label h3"><span class="label">Figure 2f</span></div><div class="figure-caption mb-2"><span class="caption"><p>Landslide conditioning factors – NDVI</p></span></div></div></div></div><div class="figure-wrapper" id="j_geo-2019-0056_fig_008_w2aab3b8d108b1b7b1ab1b2b4b9Aa"><div class="figure w-100"><div class="graphic"><img loading="lazy" src="/document/doi/10.1515/geo-2019-0056/asset/graphic/j_geo-2019-0056_fig_008.jpg" alt="Figure 2g Landslide conditioning factors – plan curvature"></img></div><div class="figure-description mb-3"><div class="figure-label h3"><span class="label">Figure 2g</span></div><div class="figure-caption mb-2"><span class="caption"><p>Landslide conditioning factors – plan curvature</p></span></div></div></div></div><div class="figure-wrapper" id="j_geo-2019-0056_fig_009_w2aab3b8d108b1b7b1ab1b2b4c10Aa"><div class="figure w-100"><div class="graphic"><img loading="lazy" src="/document/doi/10.1515/geo-2019-0056/asset/graphic/j_geo-2019-0056_fig_009.jpg" alt="Figure 2h Landslide conditioning factors – rainfall"></img></div><div class="figure-description mb-3"><div class="figure-label h3"><span class="label">Figure 2h</span></div><div class="figure-caption mb-2"><span class="caption"><p>Landslide conditioning factors – rainfall</p></span></div></div></div></div><div class="figure-wrapper" id="j_geo-2019-0056_fig_010_w2aab3b8d108b1b7b1ab1b2b4c11Aa"><div class="figure w-100"><div class="graphic"><img loading="lazy" src="/document/doi/10.1515/geo-2019-0056/asset/graphic/j_geo-2019-0056_fig_010.jpg" alt="Figure 2i Landslide conditioning factors – profile curvature"></img></div><div class="figure-description mb-3"><div class="figure-label h3"><span class="label">Figure 2i</span></div><div class="figure-caption mb-2"><span class="caption"><p>Landslide conditioning factors – profile curvature</p></span></div></div></div></div><div class="figure-wrapper" id="j_geo-2019-0056_fig_011_w2aab3b8d108b1b7b1ab1b2b4c12Aa"><div class="figure w-100"><div class="graphic"><img loading="lazy" src="/document/doi/10.1515/geo-2019-0056/asset/graphic/j_geo-2019-0056_fig_011.jpg" alt="Figure 2j Landslide conditioning factors – slope"></img></div><div class="figure-description mb-3"><div class="figure-label h3"><span class="label">Figure 2j</span></div><div class="figure-caption mb-2"><span class="caption"><p>Landslide conditioning factors – slope</p></span></div></div></div></div></section><section id="j_geo-2019-0056_s_003_s_004_w2aab3b8d108b1b7b1ab1b2b5Aa"><h3 class="subheading">3.4 Slope angle</h3><p>As one of most important factor of landslide susceptibility mapping, slope angle is usually used in landslide susceptibility mapping [<a href="#j_geo-2019-0056_ref_026_w2aab3b8d108b1b7b1ab2ac26Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_026_w2aab3b8d108b1b7b1ab2ac26Aa" data-bs-toggle="tooltip" title="[26] Regmi AD, Devkota KC, Yoshida K, et al. Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arabian Journal of Geosciences 2014;7:725-42.10.1007/s12517-012-0807-zSearch in Google Scholar">26</a>, <a href="#j_geo-2019-0056_ref_028_w2aab3b8d108b1b7b1ab2ac28Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_028_w2aab3b8d108b1b7b1ab2ac28Aa" data-bs-toggle="tooltip" title="[28] Vakhshoori V, Zare M. Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods. Geomatics, Natural Hazards and Risk 2016;7:1731-52.10.1080/19475705.2016.1144655Search in Google Scholar">28</a>, <a href="#j_geo-2019-0056_ref_051_w2aab3b8d108b1b7b1ab2ac51Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_051_w2aab3b8d108b1b7b1ab2ac51Aa" data-bs-toggle="tooltip" title="[51] Park N-W. Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis. Environmental Earth Sciences 2011;62:367-76.10.1007/s12665-010-0531-5Search in Google Scholar">51</a>]. The slope angle of the study area ranges between 0 and 60<sup>∘</sup>, it was reclassified into six classes with 10<sup>∘</sup> interval: <10<sup>∘</sup>, 10<sup>∘</sup>-20<sup>∘</sup>, 20<sup>∘</sup>-30<sup>∘</sup>, 30<sup>∘</sup>-40<sup>∘</sup>, 40<sup>∘</sup>-50<sup>∘</sup> and >50<sup>∘</sup> (<a href="#j_geo-2019-0056_fig_011_w2aab3b8d108b1b7b1ab1b2b4c12Aa" class="link link-fig" data-bs-target="j_geo-2019-0056_fig_011_w2aab3b8d108b1b7b1ab1b2b4c12Aa">Figure 2j).</a></p></section><section id="j_geo-2019-0056_s_003_s_005_w2aab3b8d108b1b7b1ab1b2b6Aa"><h3 class="subheading">3.5 Slope aspect</h3><p>Slope aspect is the direction of the slope angle and is considered as a landslide conditioning factor in several researches [<a href="#j_geo-2019-0056_ref_055_w2aab3b8d108b1b7b1ab2ac55Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_055_w2aab3b8d108b1b7b1ab2ac55Aa" data-bs-toggle="tooltip" title="[55] Pourghasemi HR, Pradhan B, Gokceoglu C. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural hazards 2012;63:965-96.10.1007/s11069-012-0217-2Search in Google Scholar">55</a>], due to numerous conditions such as weight of slope exposure to sunlight, cold and hot winds, rainfall and discontinuities [<a href="#j_geo-2019-0056_ref_052_w2aab3b8d108b1b7b1ab2ac52Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_052_w2aab3b8d108b1b7b1ab2ac52Aa" data-bs-toggle="tooltip" title="[52] Yalcin A, Reis S, Aydinoglu A, Yomralioglu T. A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 2011;85:274-87.10.1016/j.catena.2011.01.014Search in Google Scholar">52</a>, <a href="#j_geo-2019-0056_ref_056_w2aab3b8d108b1b7b1ab2ac56Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_056_w2aab3b8d108b1b7b1ab2ac56Aa" data-bs-toggle="tooltip" title="[56] Süzen ML, Doyuran V. Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey. Engineering Geology 2004;71:303-21.10.1016/S0013-7952(03)00143-1Search in Google Scholar">56</a>, <a href="#j_geo-2019-0056_ref_057_w2aab3b8d108b1b7b1ab2ac57Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_057_w2aab3b8d108b1b7b1ab2ac57Aa" data-bs-toggle="tooltip" title="[57] Komac M. A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 2006;74:17-28.10.1016/j.geomorph.2005.07.005Search in Google Scholar">57</a>]. The slope aspect is derived from DEM in ARCGIS software and reclassified into nine classes: flat (−1), north (0<sup>∘</sup>-22.5<sup>∘</sup>, 337.5<sup>∘</sup>-360<sup>∘</sup>), northeast (22.5<sup>∘</sup>-67.5<sup>∘</sup>), east (67.5<sup>∘</sup>-112.5<sup>∘</sup>), southeast (112.5<sup>∘</sup>-157.5<sup>∘</sup>), south (157.5<sup>∘</sup>-202.5<sup>∘</sup>), southwest (202.5<sup>∘</sup>-</p><p>247.5<sup>∘</sup>), west (247.5<sup>∘</sup>-292.5<sup>∘</sup>) and northwest (292.5<sup>∘</sup>-337.5<sup>∘</sup>) (<a href="#j_geo-2019-0056_fig_002_w2aab3b8d108b1b7b1ab1b2b4b3Aa" class="link link-fig" data-bs-target="j_geo-2019-0056_fig_002_w2aab3b8d108b1b7b1ab1b2b4b3Aa">Figure 2a).</a></p></section><section id="j_geo-2019-0056_s_003_s_006_w2aab3b8d108b1b7b1ab1b2b7Aa"><h3 class="subheading">3.6 Plan curvature</h3><p>Plan curvature is a geometrical parameter of the earth surface; it describes the slope change in inclination or aspect [<a href="#j_geo-2019-0056_ref_058_w2aab3b8d108b1b7b1ab2ac58Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_058_w2aab3b8d108b1b7b1ab2ac58Aa" data-bs-toggle="tooltip" title="[58] Nefeslioglu HA, Duman TY, Durmaz S. Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey). Geomorphology 2008;94:401-18.10.1016/j.geomorph.2006.10.036Search in Google Scholar">58</a>]. Plan curvature was also derived from DEM (30x30) and reclassified into five classes (natural break from Jenks) : <-0.74 (very low), from -0.74 to-0.23 (low), from −0.23 to 0.16 (moderate), from 0.16 to 0.67 (high), >0.67 (very high) (<a href="#j_geo-2019-0056_fig_008_w2aab3b8d108b1b7b1ab1b2b4b9Aa" class="link link-fig" data-bs-target="j_geo-2019-0056_fig_008_w2aab3b8d108b1b7b1ab1b2b4b9Aa">Figure 2g).</a></p></section><section id="j_geo-2019-0056_s_003_s_007_w2aab3b8d108b1b7b1ab1b2b8Aa"><h3 class="subheading">3.7 Profile curvature</h3><p>The curvature in the vertical plane parallel to the slope direction is considered as the profile curvature and it was usually used in susceptibility mapping [<a href="#j_geo-2019-0056_ref_059_w2aab3b8d108b1b7b1ab2ac59Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_059_w2aab3b8d108b1b7b1ab2ac59Aa" data-bs-toggle="tooltip" title="[59] Yilmaz C, Topal T, Süzen ML. GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey). Environmental earth sciences 2012;65:2161-78.10.1007/s12665-011-1196-4Search in Google Scholar">59</a>]. Profile curvature was also derived from DEM and reclassified into five classes (natural break) :<−0.99 (very low), from −0.99 to −0.34 (low), from−0.34 to 0.16 (moderate), from0.16 to 0.81 (high) and >0.81 (very high) (<a href="#j_geo-2019-0056_fig_007_w2aab3b8d108b1b7b1ab1b2b4b8Aa" class="link link-fig" data-bs-target="j_geo-2019-0056_fig_007_w2aab3b8d108b1b7b1ab1b2b4b8Aa">Figure 2i).</a></p></section><section id="j_geo-2019-0056_s_003_s_008_w2aab3b8d108b1b7b1ab1b2b9Aa"><h3 class="subheading">3.8 Distance to drainage network</h3><p>Rivers and drainage networks play an important role in landslide occurrence since they accumulate waters and saturate the surrounded surface and subsurface area [<a href="#j_geo-2019-0056_ref_039_w2aab3b8d108b1b7b1ab2ac39Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_039_w2aab3b8d108b1b7b1ab2ac39Aa" data-bs-toggle="tooltip" title="[39] Park S, Choi C, Kim B, Kim J. Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environmental earth sciences 2013;68:1443-64.10.1007/s12665-012-1842-5Search in Google Scholar">39</a>, <a href="#j_geo-2019-0056_ref_060_w2aab3b8d108b1b7b1ab2ac60Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_060_w2aab3b8d108b1b7b1ab2ac60Aa" data-bs-toggle="tooltip" title="[60] Nagarajan R, Roy A, Kumar RV, Mukherjee A, Khire M. Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bulletin of Engineering Geology and the Environment 2000;58:275-87.10.1007/s100649900032Search in Google Scholar">60</a>, <a href="#j_geo-2019-0056_ref_061_w2aab3b8d108b1b7b1ab2ac61Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_061_w2aab3b8d108b1b7b1ab2ac61Aa" data-bs-toggle="tooltip" title="[61] Demir G, Aytekin M, Akgün A, Ikizler SB, Tatar O. A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods. Natural hazards 2013;65:1481-506.10.1007/s11069-012-0418-8Search in Google Scholar">61</a>]. In this study, a drainage network was derived from DEM, and then the distance to drainage was generated by Euclidean distance in ARCGIS 10.4 software. Finally, the distance to drainage was reclassified into six classes with a 100m interval: <100m, 100m-200m, 200m-300m, 300m-400m, 400m-500m, >500m (<a href="#j_geo-2019-0056_fig_012_w2aab3b8d108b1b7b1ab1b2b9b3Aa" class="link link-fig" data-bs-target="j_geo-2019-0056_fig_012_w2aab3b8d108b1b7b1ab1b2b9b3Aa">Figure 2k).</a></p><div class="figure-wrapper" id="j_geo-2019-0056_fig_012_w2aab3b8d108b1b7b1ab1b2b9b3Aa"><div class="figure w-100"><div class="graphic"><img loading="lazy" src="/document/doi/10.1515/geo-2019-0056/asset/graphic/j_geo-2019-0056_fig_012.jpg" alt="Figure 2k Landslide conditioning factors – distance to drainage network"></img></div><div class="figure-description mb-3"><div class="figure-label h3"><span class="label">Figure 2k</span></div><div class="figure-caption mb-2"><span class="caption"><p>Landslide conditioning factors – distance to drainage network</p></span></div></div></div></div></section><section id="j_geo-2019-0056_s_003_s_009_w2aab3b8d108b1b7b1ab1b2c10Aa"><h3 class="subheading">3.9 NDVI</h3><p>The normalized difference vegetation index (NDVI) was extracted from Sentinel 2A satellite image [<a href="#j_geo-2019-0056_ref_028_w2aab3b8d108b1b7b1ab2ac28Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_028_w2aab3b8d108b1b7b1ab2ac28Aa" data-bs-toggle="tooltip" title="[28] Vakhshoori V, Zare M. Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods. Geomatics, Natural Hazards and Risk 2016;7:1731-52.10.1080/19475705.2016.1144655Search in Google Scholar">28</a>] and calculated by the following equation:</p><div class="formula" id="j_geo-2019-0056_eq_001_w2aab3b8d108b1b7b1ab1b2c10b3Aa"><span class="label">(1)</span><span class="alternatives"><mml:math xmlns:env="http://degruyter.com/resources/metadata" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dgror="http://degruyter.com/resources/fetched-ror-id" xmlns:m="http://degruyter.com/resources/metadata" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:tei="http://www.tei-c.org/ns/1.0"><mml:mrow><mml:mi>N</mml:mi><mml:mi>D</mml:mi><mml:mi>V</mml:mi><mml:mi>I</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>I</mml:mi><mml:mi>R</mml:mi><mml:mo>−</mml:mo><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mi>I</mml:mi><mml:mi>R</mml:mi><mml:mo>+</mml:mo><mml:mi>R</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:math></span></div><p>Where, <em>IR</em> is the infrared and R is the red bands of the electromagnetic spectrum. In this study, NDVI varies from −0.11 to 0.48 and it was reclassified into five classes (natural breaks from Jenks) : <0 (very low), 0-0.32 (low), 0.320.48 (moderate), 0.48-0.61 (high) and >0.61 (very high) (<a href="#j_geo-2019-0056_fig_007_w2aab3b8d108b1b7b1ab1b2b4b8Aa" class="link link-fig" data-bs-target="j_geo-2019-0056_fig_007_w2aab3b8d108b1b7b1ab1b2b4b8Aa">Figure 2f).</a></p></section><section id="j_geo-2019-0056_s_003_s_010_w2aab3b8d108b1b7b1ab1b2c11Aa"><h3 class="subheading">3.10 Land use/cover</h3><p>The land use/cover map of the study area was derived from the interpretation of Sentinel 2A satellite image using the semi automatic classification plugin in Qgis [<a href="#j_geo-2019-0056_ref_062_w2aab3b8d108b1b7b1ab2ac62Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_062_w2aab3b8d108b1b7b1ab2ac62Aa" data-bs-toggle="tooltip" title="[62] Congedo L. Semi-automatic classification plugin documentation. Release 2016;4:29.Search in Google Scholar">62</a>] and also based on Regional Commissariat for Agricultural Development of Jendouba [<a href="#j_geo-2019-0056_ref_063_w2aab3b8d108b1b7b1ab2ac63Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_063_w2aab3b8d108b1b7b1ab2ac63Aa" data-bs-toggle="tooltip" title="[63] RCADJ. http://www.agriculture.tn/?page_id=648Search in Google Scholar">63</a>] maps and data. The land use/cover map was reclassified into four classes: forest, cultivated area, bare soil and built up (<a href="#j_geo-2019-0056_fig_005_w2aab3b8d108b1b7b1ab1b2b4b6Aa" class="link link-fig" data-bs-target="j_geo-2019-0056_fig_005_w2aab3b8d108b1b7b1ab1b2b4b6Aa">Figure 2d).</a></p></section><section id="j_geo-2019-0056_s_003_s_011_w2aab3b8d108b1b7b1ab1b2c12Aa"><h3 class="subheading">3.11 Distance to fault</h3><p>The strength of rocks decreases with the amount of joints, which increase with the distance to faults. Thus, the distance to fault was considered as landslide susceptibility mapping factor [<a href="#j_geo-2019-0056_ref_048_w2aab3b8d108b1b7b1ab2ac48Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_048_w2aab3b8d108b1b7b1ab2ac48Aa" data-bs-toggle="tooltip" title="[48] Van Westen CJ, Castellanos E, Kuriakose SL. Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Engineering geology 2008;102:112-31.10.1016/j.enggeo.2008.03.010Search in Google Scholar">48</a>, <a href="#j_geo-2019-0056_ref_064_w2aab3b8d108b1b7b1ab2ac64Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_064_w2aab3b8d108b1b7b1ab2ac64Aa" data-bs-toggle="tooltip" title="[64] Eker R, Aydın A. Evaluation of forest roads conditions in terms of landslide susceptibility in Gölyaka and Kardüz Forest Districts (Düzce-Turkey). Eur J Forest Eng 2016;2:54-60.Search in Google Scholar">64</a>].</p><p>Fault map was derived from geological map of the National Office of Mines [<a href="#j_geo-2019-0056_ref_065_w2aab3b8d108b1b7b1ab2ac65Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_065_w2aab3b8d108b1b7b1ab2ac65Aa" data-bs-toggle="tooltip" title="[65] NOM. http://www.onm.nat.tn/en/index.phpSearch in Google Scholar">65</a>], the Euclidean distance was applied to generate the distance to fault map, then reclassified into six classes with 1000m of interval: <1000m, 1000m-2000m, 2000m-3000m, 3000m-4000m, 4000m-5000m, >5000m (<a href="#j_geo-2019-0056_fig_004_w2aab3b8d108b1b7b1ab1b2b4b5Aa" class="link link-fig" data-bs-target="j_geo-2019-0056_fig_004_w2aab3b8d108b1b7b1ab1b2b4b5Aa">Figure 2c).</a></p></section><section id="j_geo-2019-0056_s_003_s_012_w2aab3b8d108b1b7b1ab1b2c13Aa"><h3 class="subheading">3.12 Lithology</h3><p>The lithology has an important impact on slope stability, the different lithological units have different susceptibility degree [<a href="#j_geo-2019-0056_ref_066_w2aab3b8d108b1b7b1ab2ac66Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_066_w2aab3b8d108b1b7b1ab2ac66Aa" data-bs-toggle="tooltip" title="[66] Dai F, Lee C, Li J, Xu Z. Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology 2001;40:381-91.10.1007/s002540000163Search in Google Scholar">66</a>, <a href="#j_geo-2019-0056_ref_067_w2aab3b8d108b1b7b1ab2ac67Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_067_w2aab3b8d108b1b7b1ab2ac67Aa" data-bs-toggle="tooltip" title="[67] Varnes DJ. Landslide hazard zonation: a review of principles and practice1984.Search in Google Scholar">67</a>, <a href="#j_geo-2019-0056_ref_068_w2aab3b8d108b1b7b1ab2ac68Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_068_w2aab3b8d108b1b7b1ab2ac68Aa" data-bs-toggle="tooltip" title="[68] Hong H, Pradhan B, Bui DT, Xu C, Youssef AM, Chen W. Comparison of four kernel functions used in support vector machines for landslide susceptibility mapping: a case study at Suichuan area (China). Geomatics, Natural Hazards and Risk 2017;8:544-69.10.1080/19475705.2016.1250112Search in Google Scholar">68</a>]; for example, clay unit is more prone to fail than calcareous unit. With this logic in mind, the lithological map was derived from the geological map and was reclassified into four classes: clay and marl units, clay and sand units, sand and evaporates units and limestone and calcareous units from the most to the least susceptible, respectively (<a href="#j_geo-2019-0056_fig_006_w2aab3b8d108b1b7b1ab1b2b4b7Aa" class="link link-fig" data-bs-target="j_geo-2019-0056_fig_006_w2aab3b8d108b1b7b1ab1b2b4b7Aa">Figure 2e).</a></p></section><section id="j_geo-2019-0056_s_003_s_013_w2aab3b8d108b1b7b1ab1b2c14Aa"><h3 class="subheading">3.13 Rainfall</h3><p>Rainfall is considered as the landslide triggering factor. It plays an important role in shear strength decrease by increasing pore pressure [<a href="#j_geo-2019-0056_ref_069_w2aab3b8d108b1b7b1ab2ac69Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_069_w2aab3b8d108b1b7b1ab2ac69Aa" data-bs-toggle="tooltip" title="[69] Yalcin A. The effects of clay on landslides: A case study. Applied Clay Science 2007;38:77-85.10.1016/j.clay.2007.01.007Search in Google Scholar">69</a>]. Thus, rainfall is usually used in susceptibility analysis [<a href="#j_geo-2019-0056_ref_028_w2aab3b8d108b1b7b1ab2ac28Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_028_w2aab3b8d108b1b7b1ab2ac28Aa" data-bs-toggle="tooltip" title="[28] Vakhshoori V, Zare M. Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods. Geomatics, Natural Hazards and Risk 2016;7:1731-52.10.1080/19475705.2016.1144655Search in Google Scholar">28</a>, <a href="#j_geo-2019-0056_ref_070_w2aab3b8d108b1b7b1ab2ac70Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_070_w2aab3b8d108b1b7b1ab2ac70Aa" data-bs-toggle="tooltip" title="[70] Hadji R, errahmane Boumazbeur A, Limani Y, Baghem M, el Madjid Chouabi A, Demdoum A. Geologic, topographic and climatic controls in landslide hazard assessment using GIS modeling: a case study of Souk Ahras region, NE Algeria. Quaternary International 2013;302:224-37.10.1016/j.quaint.2012.11.027Search in Google Scholar">70</a>, <a href="#j_geo-2019-0056_ref_071_w2aab3b8d108b1b7b1ab2ac71Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_071_w2aab3b8d108b1b7b1ab2ac71Aa" data-bs-toggle="tooltip" title="[71] Hadji R, Limani Y, Demdoum A. Using multivariate approach and GIS applications to predict slope instability hazard case study of Machrouha municipality, NE Algeria. Information and Communication Technologies for Disaster Management (ICT-DM), 2014 1st International Conference on; 2014: IEEE. p. 1-10.10.1109/ICT-DM.2014.6917787Search in Google Scholar">71</a>]. The annual average precipitation map was produced by kriging data of meteorological stations available in Tabarka and Ain-Drahim delegations Then, reclassified into six classes with 100mm/year interval: <800, 800-900, 900-1000, 1000-1100, 1100-1200, >1200 mm/year (<a href="#j_geo-2019-0056_fig_009_w2aab3b8d108b1b7b1ab1b2b4c10Aa" class="link link-fig" data-bs-target="j_geo-2019-0056_fig_009_w2aab3b8d108b1b7b1ab1b2b4c10Aa">Figure 2h).</a></p></section></section><section id="j_geo-2019-0056_s_004_w2aab3b8d108b1b7b1ab1b3Aa"><h2 class="subheading">4 Methodology</h2><p>In this study two statistical bivariate models: evidential belief function (EBF) and weight of evidence (WoE) were used to produce landslide susceptibility maps using ARCGIS 10.4 as GIS software.</p><section id="j_geo-2019-0056_s_004_s_001_w2aab3b8d108b1b7b1ab1b3b3Aa"><h3 class="subheading">4.1 Evidential belief function (EBF)</h3><p>The theory of belief functions is a statistical bivariate model known as Dempster-Shafer theory [<a href="#j_geo-2019-0056_ref_072_w2aab3b8d108b1b7b1ab2ac72Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_072_w2aab3b8d108b1b7b1ab2ac72Aa" data-bs-toggle="tooltip" title="[72] Dempster AP. Upper and lower probabilities induced by a multivalued mapping. The annals of mathematical statistics 1967:325-39.10.1007/978-3-540-44792-4_3Search in Google Scholar">72</a>, <a href="#j_geo-2019-0056_ref_073_w2aab3b8d108b1b7b1ab2ac73Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_073_w2aab3b8d108b1b7b1ab2ac73Aa" data-bs-toggle="tooltip" title="[73] Shafer G. A mathematical theory of evidence: Princeton university press; 1976.10.1515/9780691214696Search in Google Scholar">73</a>]. The evidential belief function has been used in landslide susceptibility mapping by many researchers [<a href="#j_geo-2019-0056_ref_006_w2aab3b8d108b1b7b1ab2ab6Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_006_w2aab3b8d108b1b7b1ab2ab6Aa" data-bs-toggle="tooltip" title="[6] Cui K, Lu D, Li W.Comparison of landslide susceptibility mappingbased on statistical index, certainty factors, weights of evidence and evidential belief function models. Geocarto International 2017;32:935-55.10.1080/10106049.2016.1195886Search in Google Scholar">6</a>, <a href="#j_geo-2019-0056_ref_030_w2aab3b8d108b1b7b1ab2ac30Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_030_w2aab3b8d108b1b7b1ab2ac30Aa" data-bs-toggle="tooltip" title="[30] Pradhan B, Abokharima MH, Jebur MN, Tehrany MS. Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Natural hazards 2014;73:1019-42.10.1007/s11069-014-1128-1Search in Google Scholar">30</a>, <a href="#j_geo-2019-0056_ref_032_w2aab3b8d108b1b7b1ab2ac32Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_032_w2aab3b8d108b1b7b1ab2ac32Aa" data-bs-toggle="tooltip" title="[32] Zhang Z, Yang F, Chen H, et al. GIS-based landslide susceptibility analysis using frequency ratio and evidential belief function models. Environmental Earth Sciences 2016;75:948.10.1007/s12665-016-5732-0Search in Google Scholar">32</a>]. The EBF model is defined by four statistical functions: Bel (degree of belief) which means the lower degree of belief for each factor, Dis (degree of disbelief) which means the degree of disbelief for each factor,Unc (degree of uncertainty) which means the degree of uncertainty for each factor and Pls (degree of plausibility) which means the upper limits of the probability. The data driven estimation of the evidential belief functions can be calculated by many equations; in this study, the equations used by researchers which include [<a href="#j_geo-2019-0056_ref_031_w2aab3b8d108b1b7b1ab2ac31Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_031_w2aab3b8d108b1b7b1ab2ac31Aa" data-bs-toggle="tooltip" title="[31] Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB. Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the eflcacy of evidential belief functions and fuzzy logic models. Catena 2012;96:28-40.10.1016/j.catena.2012.04.001Search in Google Scholar">31</a>, <a href="#j_geo-2019-0056_ref_074_w2aab3b8d108b1b7b1ab2ac74Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_074_w2aab3b8d108b1b7b1ab2ac74Aa" data-bs-toggle="tooltip" title="[74] Lee S, Hwang J, Park I. Application of data-driven evidential belief functions to landslide susceptibility mapping in Jinbu, Korea. Catena 2013;100:15-30.10.1016/j.catena.2012.07.014Search in Google Scholar">74</a>] were applied.</p></section><section id="j_geo-2019-0056_s_004_s_002_w2aab3b8d108b1b7b1ab1b3b4Aa"><h3 class="subheading">4.2 Weight of evidence (WoE)</h3><p>The weight of evidence method was used for the first time in 1988 for mineral exploration [<a href="#j_geo-2019-0056_ref_075_w2aab3b8d108b1b7b1ab2ac75Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_075_w2aab3b8d108b1b7b1ab2ac75Aa" data-bs-toggle="tooltip" title="[75] Bonham-Carter G, Agterberg F,Wright D. Integration of geological datasets for gold exploration in Nova Scotia. Digital Geologic and Geographic Information Systems 1988:15-23.10.1029/SC010p0015Search in Google Scholar">75</a>] and in 2003 for landslide susceptibility mapping [<a href="#j_geo-2019-0056_ref_076_w2aab3b8d108b1b7b1ab2ac76Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_076_w2aab3b8d108b1b7b1ab2ac76Aa" data-bs-toggle="tooltip" title="[76] Van Westen C, Rengers N, Soeters R. Use of geomorphological information in indirect landslide susceptibility assessment. Natural hazards 2003;30:399-419.10.1023/B:NHAZ.0000007097.42735.9eSearch in Google Scholar">76</a>]. Then, the WoE method was widely used by researchers [<a href="#j_geo-2019-0056_ref_006_w2aab3b8d108b1b7b1ab2ab6Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_006_w2aab3b8d108b1b7b1ab2ab6Aa" data-bs-toggle="tooltip" title="[6] Cui K, Lu D, Li W.Comparison of landslide susceptibility mappingbased on statistical index, certainty factors, weights of evidence and evidential belief function models. Geocarto International 2017;32:935-55.10.1080/10106049.2016.1195886Search in Google Scholar">6</a>, <a href="#j_geo-2019-0056_ref_077_w2aab3b8d108b1b7b1ab2ac77Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_077_w2aab3b8d108b1b7b1ab2ac77Aa" data-bs-toggle="tooltip" title="[77] Neuhäuser B, Damm B, Terhorst B. GIS-based assessment of landslide susceptibility on the base of the weights-of-evidencemodel. Landslides 2012;9:511-28.10.1007/s10346-011-0305-5Search in Google Scholar">77</a>, <a href="#j_geo-2019-0056_ref_078_w2aab3b8d108b1b7b1ab2ac78Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_078_w2aab3b8d108b1b7b1ab2ac78Aa" data-bs-toggle="tooltip" title="[78] Pradhan B, Oh H-J, Buchroithner M. Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area. Geomatics, Natural Hazards and Risk 2010;1:199-223.10.1080/19475705.2010.498151Search in Google Scholar">78</a>, <a href="#j_geo-2019-0056_ref_079_w2aab3b8d108b1b7b1ab2ac79Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_079_w2aab3b8d108b1b7b1ab2ac79Aa" data-bs-toggle="tooltip" title="[79] Chiu CF, Yan WM, Yuen K-V. Estimation of water retention curve of granular soils from particle-size distribution—a Bayesian probabilistic approach. Canadian Geotechnical Journal 2012;49:1024-35.10.1139/t2012-062Search in Google Scholar">79</a>, <a href="#j_geo-2019-0056_ref_080_w2aab3b8d108b1b7b1ab2ac80Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_080_w2aab3b8d108b1b7b1ab2ac80Aa" data-bs-toggle="tooltip" title="[80] Cao Z, Wang Y. Bayesian model comparison and selection of spatial correlation functions for soil parameters. Structural Safety 2014;49:10-7.10.1016/j.strusafe.2013.06.003Search in Google Scholar">80</a>]. The WoE method is a probabilistic method based on the following Bayes’ rule equations:</p><div class="formula" id="j_geo-2019-0056_eq_002_w2aab3b8d108b1b7b1ab1b3b4b3Aa"><span class="label">(2)</span><span class="alternatives"><mml:math xmlns:env="http://degruyter.com/resources/metadata" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dgror="http://degruyter.com/resources/fetched-ror-id" xmlns:m="http://degruyter.com/resources/metadata" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:tei="http://www.tei-c.org/ns/1.0"><mml:mrow><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>A</mml:mi><mml:mi>B</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>B</mml:mi><mml:mi>A</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>×</mml:mo><mml:mfrac><mml:mrow><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi>A</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>P</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi>B</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:mfrac></mml:mrow></mml:math></span></div></section><section id="j_geo-2019-0056_s_004_s_003_w2aab3b8d108b1b7b1ab1b3b5Aa"><h3 class="subheading">4.3 Validation of landslide susceptibility models</h3><p>After elaborating the landslide susceptibility map using different models, their validation is necessary in order to check their reliability, to compare the results of these models and to choose the best one. There are many method of model validation such as: success/ prediction rate curve,landslide density or frequency, Chi squared, etc. The success/prediction rate curve is the most common method followed by landslide density or frequency [<a href="#j_geo-2019-0056_ref_081_w2aab3b8d108b1b7b1ab2ac81Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_081_w2aab3b8d108b1b7b1ab2ac81Aa" data-bs-toggle="tooltip" title="[81] Reichenbach P, Rossi M, Malamud B, Mihir M, Guzzetti F. A review of statistically-based landslide susceptibility models. Earth-Science Reviews 2018.10.1016/j.earscirev.2018.03.001Search in Google Scholar">81</a>]. In this study, both success and predictive rate curves using the area under the receiver operating characteristic curve (AUROC) were applied.</p><p>The success rate curve allow to check how well the resultant map has classified the areas of existing landslides [<a href="#j_geo-2019-0056_ref_082_w2aab3b8d108b1b7b1ab2ac82Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_082_w2aab3b8d108b1b7b1ab2ac82Aa" data-bs-toggle="tooltip" title="[82] Chung C-JF, Fabbri AG. Probabilistic prediction models for landslide hazardmapping. Photogrammetric engineering and remote sensing 1999;65:1389-99.Search in Google Scholar">82</a>]. The success rate curve was obtained by comparing the training dataset with the landslide susceptibility map.</p><p>The prediction rate curve indicates the model efficiency to predict future landslide [<a href="#j_geo-2019-0056_ref_017_w2aab3b8d108b1b7b1ab2ac17Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_017_w2aab3b8d108b1b7b1ab2ac17Aa" data-bs-toggle="tooltip" title="[17] Pradhan B, Lee S. Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Sciences 2010;60:1037-54.10.1007/s12665-009-0245-8Search in Google Scholar">17</a>, <a href="#j_geo-2019-0056_ref_083_w2aab3b8d108b1b7b1ab2ac83Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_083_w2aab3b8d108b1b7b1ab2ac83Aa" data-bs-toggle="tooltip" title="[83] Chung C-JF, Fabbri AG. Validation of spatial prediction models for landslide hazard mapping. Natural Hazards 2003;30:451-72.10.1023/B:NHAZ.0000007172.62651.2bSearch in Google Scholar">83</a>]. The comparison of the validation dataset with the landslide susceptibility map provides the prediction rate curve.</p></section></section><section id="j_geo-2019-0056_s_005_w2aab3b8d108b1b7b1ab1b4Aa"><h2 class="subheading">5 Results</h2><section id="j_geo-2019-0056_s_005_s_001_w2aab3b8d108b1b7b1ab1b4b2Aa"><h3 class="subheading">5.1 Conditioning factors</h3><p>The weights of all classes of all conditioning factors calculated with the EBF and WoE models are presented in the first table (<a href="#j_geo-2019-0056_tab_001_w2aab3b8d108b1b7b1ab1b4b2b3Aa" class="link link-table" data-bs-target="j_geo-2019-0056_tab_001_w2aab3b8d108b1b7b1ab1b4b2b3Aa">Table 1</a>). Results show a good correlation between the weights of each class for the two models. This indicates that the susceptibility of each class is similar for all methods.</p><div class="table-wrap mb-4" id="j_geo-2019-0056_tab_001_w2aab3b8d108b1b7b1ab1b4b2b3Aa" position="float" orientation="portrait"><div class="table-label h3">Table 1</div><div class="caption mb-3"><p>Spatial relationship between each landslide conditioning factor and landslide by EBF and WoE models.</p></div><table xmlns:env="http://degruyter.com/resources/metadata" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dgror="http://degruyter.com/resources/fetched-ror-id" xmlns:m="http://degruyter.com/resources/metadata" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:tei="http://www.tei-c.org/ns/1.0" rules="groups" frame="hsides" class="content-table"><thead><tr><th style="text-align: center">Factor</th><th style="text-align: center">Class</th><th style="text-align: center">N. of class pixels</th><th style="text-align: center">N. of landslide pixels</th><th style="text-align: center">Percentage of class</th><th style="text-align: center">Percentage of landslide</th><th style="text-align: center">Bel</th><th style="text-align: center">Dis</th><th style="text-align: center">Une</th><th style="text-align: center">Pis</th><th style="text-align: center">C</th><th style="text-align: center">S(C)</th><th style="text-align: center">C/ S(C)</th></tr></thead><tbody><tr><td style="text-align: center">Aspect</td><td style="text-align: center">Flat</td><td style="text-align: center">2177</td><td style="text-align: center">1</td><td style="text-align: center">0.19</td><td style="text-align: center">0.13</td><td style="text-align: center">0.071</td><td style="text-align: center">0.111</td><td style="text-align: center">0.818</td><td style="text-align: center">0.889</td><td style="text-align: center">-0.442</td><td style="text-align: center">1.001</td><td style="text-align: center">-0.441</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">N</td><td style="text-align: center">172221</td><td style="text-align: center">75</td><td style="text-align: center">15.39</td><td style="text-align: center">9.39</td><td style="text-align: center">0.067</td><td style="text-align: center">0.119</td><td style="text-align: center">0.814</td><td style="text-align: center">0.881</td><td style="text-align: center">-0.563</td><td style="text-align: center">0.121</td><td style="text-align: center">-4.638</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">NE</td><td style="text-align: center">124452</td><td style="text-align: center">47</td><td style="text-align: center">11.12</td><td style="text-align: center">5.88</td><td style="text-align: center">0.058</td><td style="text-align: center">0.118</td><td style="text-align: center">0.824</td><td style="text-align: center">0.882</td><td style="text-align: center">-0.694</td><td style="text-align: center">0.150</td><td style="text-align: center">-4.615</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">E</td><td style="text-align: center">132827</td><td style="text-align: center">124</td><td style="text-align: center">11.87</td><td style="text-align: center">15.52</td><td style="text-align: center">0.144</td><td style="text-align: center">0.106</td><td style="text-align: center">0.750</td><td style="text-align: center">0.894</td><td style="text-align: center">0.311</td><td style="text-align: center">0.098</td><td style="text-align: center">3.177</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">SE</td><td style="text-align: center">134463</td><td style="text-align: center">120</td><td style="text-align: center">12.01</td><td style="text-align: center">15.02</td><td style="text-align: center">0.138</td><td style="text-align: center">0.107</td><td style="text-align: center">0.755</td><td style="text-align: center">0.893</td><td style="text-align: center">0.258</td><td style="text-align: center">0.099</td><td style="text-align: center">2.604</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">S</td><td style="text-align: center">122931</td><td style="text-align: center">106</td><td style="text-align: center">10.98</td><td style="text-align: center">13.27</td><td style="text-align: center">0.133</td><td style="text-align: center">0.108</td><td style="text-align: center">0.759</td><td style="text-align: center">0.892</td><td style="text-align: center">0.215</td><td style="text-align: center">0.104</td><td style="text-align: center">2.059</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">SW</td><td style="text-align: center">103423</td><td style="text-align: center">140</td><td style="text-align: center">9.24</td><td style="text-align: center">17.52</td><td style="text-align: center">0.209</td><td style="text-align: center">0.101</td><td style="text-align: center">0.690</td><td style="text-align: center">0.899</td><td style="text-align: center">0.736</td><td style="text-align: center">0.093</td><td style="text-align: center">7.899</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">W</td><td style="text-align: center">144238</td><td style="text-align: center">101</td><td style="text-align: center">12.89</td><td style="text-align: center">12.64</td><td style="text-align: center">0.108</td><td style="text-align: center">0.111</td><td style="text-align: center">0.781</td><td style="text-align: center">0.889</td><td style="text-align: center">-0.022</td><td style="text-align: center">0.106</td><td style="text-align: center">-0.208</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">NW</td><td style="text-align: center">182477</td><td style="text-align: center">85</td><td style="text-align: center">16.30</td><td style="text-align: center">10.64</td><td style="text-align: center">0.072</td><td style="text-align: center">0.119</td><td style="text-align: center">0.810</td><td style="text-align: center">0.881</td><td style="text-align: center">-0.492</td><td style="text-align: center">0.115</td><td style="text-align: center">-4.291</td></tr><tr><td style="text-align: center">DEM</td><td style="text-align: center">>200</td><td style="text-align: center">310162</td><td style="text-align: center">277</td><td style="text-align: center">27.71</td><td style="text-align: center">34.67</td><td style="text-align: center">0.287</td><td style="text-align: center">0.181</td><td style="text-align: center">0.533</td><td style="text-align: center">0.819</td><td style="text-align: center">0.325</td><td style="text-align: center">0.074</td><td style="text-align: center">4.372</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">200_400</td><td style="text-align: center">280221</td><td style="text-align: center">174</td><td style="text-align: center">25.04</td><td style="text-align: center">21.78</td><td style="text-align: center">0.199</td><td style="text-align: center">0.209</td><td style="text-align: center">0.592</td><td style="text-align: center">0.791</td><td style="text-align: center">-0.182</td><td style="text-align: center">0.086</td><td style="text-align: center">-2.124</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">400_600</td><td style="text-align: center">352146</td><td style="text-align: center">206</td><td style="text-align: center">31.46</td><td style="text-align: center">25.78</td><td style="text-align: center">0.188</td><td style="text-align: center">0.216</td><td style="text-align: center">0.596</td><td style="text-align: center">0.784</td><td style="text-align: center">-0.279</td><td style="text-align: center">0.081</td><td style="text-align: center">-3.446</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">600_800</td><td style="text-align: center">147623</td><td style="text-align: center">140</td><td style="text-align: center">13.19</td><td style="text-align: center">17.52</td><td style="text-align: center">0.304</td><td style="text-align: center">0.190</td><td style="text-align: center">0.506</td><td style="text-align: center">0.810</td><td style="text-align: center">0.335</td><td style="text-align: center">0.093</td><td style="text-align: center">3.600</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">>800</td><td style="text-align: center">29057</td><td style="text-align: center">2</td><td style="text-align: center">2.60</td><td style="text-align: center">0.25</td><td style="text-align: center">0.022</td><td style="text-align: center">0.205</td><td style="text-align: center">0.773</td><td style="text-align: center">0.795</td><td style="text-align: center">-2.363</td><td style="text-align: center">0.708</td><td style="text-align: center">-3.337</td></tr><tr><td style="text-align: center">Fault</td><td style="text-align: center">>5000</td><td style="text-align: center">193650</td><td style="text-align: center">4</td><td style="text-align: center">17.30</td><td style="text-align: center">0.50</td><td style="text-align: center">0.005</td><td style="text-align: center">0.201</td><td style="text-align: center">0.793</td><td style="text-align: center">0.799</td><td style="text-align: center">-3.728</td><td style="text-align: center">0.501</td><td style="text-align: center">-7.437</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">4000_5000</td><td style="text-align: center">88507</td><td style="text-align: center">8</td><td style="text-align: center">7.91</td><td style="text-align: center">1.00</td><td style="text-align: center">0.024</td><td style="text-align: center">0.180</td><td style="text-align: center">0.796</td><td style="text-align: center">0.820</td><td style="text-align: center">-2.139</td><td style="text-align: center">0.355</td><td style="text-align: center">-6.019</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">3000_4000</td><td style="text-align: center">109585</td><td style="text-align: center">85</td><td style="text-align: center">9.79</td><td style="text-align: center">10.64</td><td style="text-align: center">0.206</td><td style="text-align: center">0.166</td><td style="text-align: center">0.629</td><td style="text-align: center">0.834</td><td style="text-align: center">0.092</td><td style="text-align: center">0.115</td><td style="text-align: center">0.805</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">2000_3000</td><td style="text-align: center">156175</td><td style="text-align: center">134</td><td style="text-align: center">13.95</td><td style="text-align: center">16.77</td><td style="text-align: center">0.227</td><td style="text-align: center">0.162</td><td style="text-align: center">0.611</td><td style="text-align: center">0.838</td><td style="text-align: center">0.217</td><td style="text-align: center">0.095</td><td style="text-align: center">2.292</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">1000_2000</td><td style="text-align: center">247963</td><td style="text-align: center">289</td><td style="text-align: center">22.16</td><td style="text-align: center">36.17</td><td style="text-align: center">0.309</td><td style="text-align: center">0.137</td><td style="text-align: center">0.554</td><td style="text-align: center">0.863</td><td style="text-align: center">0.689</td><td style="text-align: center">0.074</td><td style="text-align: center">9.349</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">>1000</td><td style="text-align: center">323319</td><td style="text-align: center">279</td><td style="text-align: center">28.89</td><td style="text-align: center">34.92</td><td style="text-align: center">0.229</td><td style="text-align: center">0.153</td><td style="text-align: center">0.618</td><td style="text-align: center">0.847</td><td style="text-align: center">0.278</td><td style="text-align: center">0.074</td><td style="text-align: center">3.747</td></tr><tr><td style="text-align: center">Geology</td><td style="text-align: center">Limestones</td><td style="text-align: center">122056</td><td style="text-align: center">33</td><td style="text-align: center">10.91</td><td style="text-align: center">4.13</td><td style="text-align: center">0.097</td><td style="text-align: center">0.273</td><td style="text-align: center">0.629</td><td style="text-align: center">0.727</td><td style="text-align: center">-1.044</td><td style="text-align: center">0.178</td><td style="text-align: center">-5.873</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">Sand/Evaporite</td><td style="text-align: center">89705</td><td style="text-align: center">46</td><td style="text-align: center">8.02</td><td style="text-align: center">5.76</td><td style="text-align: center">0.184</td><td style="text-align: center">0.260</td><td style="text-align: center">0.555</td><td style="text-align: center">0.740</td><td style="text-align: center">-0.355</td><td style="text-align: center">0.152</td><td style="text-align: center">-2.338</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">Clay/ Sand</td><td style="text-align: center">94411</td><td style="text-align: center">119</td><td style="text-align: center">8.44</td><td style="text-align: center">14.89</td><td style="text-align: center">0.453</td><td style="text-align: center">0.236</td><td style="text-align: center">0.311</td><td style="text-align: center">0.764</td><td style="text-align: center">0.642</td><td style="text-align: center">0.099</td><td style="text-align: center">6.453</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">Clay/ Marl</td><td style="text-align: center">813001</td><td style="text-align: center">601</td><td style="text-align: center">72.64</td><td style="text-align: center">75.22</td><td style="text-align: center">0.266</td><td style="text-align: center">0.230</td><td style="text-align: center">0.504</td><td style="text-align: center">0.770</td><td style="text-align: center">0.134</td><td style="text-align: center">0.082</td><td style="text-align: center">1.632</td></tr><tr><td style="text-align: center">Land Cover</td><td style="text-align: center">Forest</td><td style="text-align: center">824871</td><td style="text-align: center">525</td><td style="text-align: center">73.71</td><td style="text-align: center">65.71</td><td style="text-align: center">0.147</td><td style="text-align: center">0.309</td><td style="text-align: center">0.543</td><td style="text-align: center">0.691</td><td style="text-align: center">-0.381</td><td style="text-align: center">0.075</td><td style="text-align: center">-5.108</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">Bare soils</td><td style="text-align: center">40397</td><td style="text-align: center">32</td><td style="text-align: center">3.61</td><td style="text-align: center">4.01</td><td style="text-align: center">0.183</td><td style="text-align: center">0.236</td><td style="text-align: center">0.581</td><td style="text-align: center">0.764</td><td style="text-align: center">0.108</td><td style="text-align: center">0.180</td><td style="text-align: center">0.598</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">Cultivated</td><td style="text-align: center">225037</td><td style="text-align: center">182</td><td style="text-align: center">20.11</td><td style="text-align: center">22.78</td><td style="text-align: center">0.187</td><td style="text-align: center">0.229</td><td style="text-align: center">0.584</td><td style="text-align: center">0.771</td><td style="text-align: center">0.159</td><td style="text-align: center">0.084</td><td style="text-align: center">1.879</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">Built up</td><td style="text-align: center">28714</td><td style="text-align: center">60</td><td style="text-align: center">2.57</td><td style="text-align: center">7.51</td><td style="text-align: center">0.483</td><td style="text-align: center">0.225</td><td style="text-align: center">0.292</td><td style="text-align: center">0.775</td><td style="text-align: center">1.126</td><td style="text-align: center">0.134</td><td style="text-align: center">8.379</td></tr><tr><td style="text-align: center">NDVI</td><td style="text-align: center">Very low</td><td style="text-align: center">11246</td><td style="text-align: center">24</td><td style="text-align: center">1.00</td><td style="text-align: center">3.00</td><td style="text-align: center">0.221</td><td style="text-align: center">0.184</td><td style="text-align: center">0.595</td><td style="text-align: center">0.816</td><td style="text-align: center">1.115</td><td style="text-align: center">0.207</td><td style="text-align: center">5.376</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">Low</td><td style="text-align: center">70754</td><td style="text-align: center">442</td><td style="text-align: center">6.32</td><td style="text-align: center">55.32</td><td style="text-align: center">0.646</td><td style="text-align: center">0.090</td><td style="text-align: center">0.265</td><td style="text-align: center">0.910</td><td style="text-align: center">2.909</td><td style="text-align: center">0.071</td><td style="text-align: center">40.825</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">Moderate</td><td style="text-align: center">216910</td><td style="text-align: center">203</td><td style="text-align: center">19.38</td><td style="text-align: center">25.41</td><td style="text-align: center">0.097</td><td style="text-align: center">0.174</td><td style="text-align: center">0.729</td><td style="text-align: center">0.826</td><td style="text-align: center">0.348</td><td style="text-align: center">0.081</td><td style="text-align: center">4.285</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">High</td><td style="text-align: center">340479</td><td style="text-align: center">101</td><td style="text-align: center">30.42</td><td style="text-align: center">12.64</td><td style="text-align: center">0.031</td><td style="text-align: center">0.236</td><td style="text-align: center">0.734</td><td style="text-align: center">0.764</td><td style="text-align: center">-1.106</td><td style="text-align: center">0.106</td><td style="text-align: center">-10.386</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">Very high</td><td style="text-align: center">479772</td><td style="text-align: center">29</td><td style="text-align: center">42.87</td><td style="text-align: center">3.63</td><td style="text-align: center">0.006</td><td style="text-align: center">0.317</td><td style="text-align: center">0.677</td><td style="text-align: center">0.683</td><td style="text-align: center">-2.992</td><td style="text-align: center">0.189</td><td style="text-align: center">-15.816</td></tr><tr><td style="text-align: center">Plan curvature</td><td style="text-align: center">Very low</td><td style="text-align: center">70102</td><td style="text-align: center">77</td><td style="text-align: center">6.26</td><td style="text-align: center">9.64</td><td style="text-align: center">0.273</td><td style="text-align: center">0.192</td><td style="text-align: center">0.535</td><td style="text-align: center">0.808</td><td style="text-align: center">0.468</td><td style="text-align: center">0.120</td><td style="text-align: center">3.898</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">Low</td><td style="text-align: center">263475</td><td style="text-align: center">196</td><td style="text-align: center">23.54</td><td style="text-align: center">24.53</td><td style="text-align: center">0.185</td><td style="text-align: center">0.196</td><td style="text-align: center">0.619</td><td style="text-align: center">0.804</td><td style="text-align: center">0.054</td><td style="text-align: center">0.082</td><td style="text-align: center">0.659</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">Moderate</td><td style="text-align: center">395776</td><td style="text-align: center">236</td><td style="text-align: center">35.36</td><td style="text-align: center">29.54</td><td style="text-align: center">0.148</td><td style="text-align: center">0.217</td><td style="text-align: center">0.635</td><td style="text-align: center">0.783</td><td style="text-align: center">-0.266</td><td style="text-align: center">0.078</td><td style="text-align: center">-3.433</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">High</td><td style="text-align: center">296301</td><td style="text-align: center">207</td><td style="text-align: center">26.47</td><td style="text-align: center">25.91</td><td style="text-align: center">0.174</td><td style="text-align: center">0.200</td><td style="text-align: center">0.626</td><td style="text-align: center">0.800</td><td style="text-align: center">-0.029</td><td style="text-align: center">0.081</td><td style="text-align: center">-0.363</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">Very high</td><td style="text-align: center">93555</td><td style="text-align: center">83</td><td style="text-align: center">8.36</td><td style="text-align: center">10.39</td><td style="text-align: center">0.220</td><td style="text-align: center">0.195</td><td style="text-align: center">0.585</td><td style="text-align: center">0.805</td><td style="text-align: center">0.240</td><td style="text-align: center">0.116</td><td style="text-align: center">2.066</td></tr><tr><td style="text-align: center">Rainfall</td><td style="text-align: center">>800</td><td style="text-align: center">78940</td><td style="text-align: center">1</td><td style="text-align: center">7.05</td><td style="text-align: center">0.13</td><td style="text-align: center">0.003</td><td style="text-align: center">0.179</td><td style="text-align: center">0.818</td><td style="text-align: center">0.821</td><td style="text-align: center">-4.104</td><td style="text-align: center">1.001</td><td style="text-align: center">-4.101</td></tr><tr><td style="text-align: center">Factor</td><td style="text-align: center">Class</td><td style="text-align: center">N. of class pixels</td><td style="text-align: center">N. of landslide pixels</td><td style="text-align: center">Percentage of class</td><td style="text-align: center">Percentage of landslide</td><td style="text-align: center">Bel</td><td style="text-align: center">Dis</td><td style="text-align: center">Une</td><td style="text-align: center">Pis</td><td style="text-align: center"></td><td style="text-align: center">S(C)</td><td style="text-align: center">C/S(C)</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">800_900</td><td style="text-align: center">206417</td><td style="text-align: center">167</td><td style="text-align: center">18.44</td><td style="text-align: center">20.90</td><td style="text-align: center">0.179</td><td style="text-align: center">0.162</td><td style="text-align: center">0.659</td><td style="text-align: center">0.838</td><td style="text-align: center">0.156</td><td style="text-align: center">0.087</td><td style="text-align: center">1.789</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">900_1000</td><td style="text-align: center">333245</td><td style="text-align: center">237</td><td style="text-align: center">29.78</td><td style="text-align: center">29.66</td><td style="text-align: center">0.158</td><td style="text-align: center">0.167</td><td style="text-align: center">0.675</td><td style="text-align: center">0.833</td><td style="text-align: center">-0.005</td><td style="text-align: center">0.077</td><td style="text-align: center">-0.070</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">1000_1100</td><td style="text-align: center">246279</td><td style="text-align: center">134</td><td style="text-align: center">22.00</td><td style="text-align: center">16.77</td><td style="text-align: center">0.121</td><td style="text-align: center">0.178</td><td style="text-align: center">0.702</td><td style="text-align: center">0.822</td><td style="text-align: center">-0.337</td><td style="text-align: center">0.095</td><td style="text-align: center">-3.553</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">1100_1200</td><td style="text-align: center">207694</td><td style="text-align: center">189</td><td style="text-align: center">18.56</td><td style="text-align: center">23.65</td><td style="text-align: center">0.202</td><td style="text-align: center">0.156</td><td style="text-align: center">0.642</td><td style="text-align: center">0.844</td><td style="text-align: center">0.307</td><td style="text-align: center">0.083</td><td style="text-align: center">3.690</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">>1200</td><td style="text-align: center">46634</td><td style="text-align: center">71</td><td style="text-align: center">4.17</td><td style="text-align: center">8.89</td><td style="text-align: center">0.338</td><td style="text-align: center">0.158</td><td style="text-align: center">0.504</td><td style="text-align: center">0.842</td><td style="text-align: center">0.808</td><td style="text-align: center">0.124</td><td style="text-align: center">6.493</td></tr><tr><td style="text-align: center">Profile curvature</td><td style="text-align: center">Very low</td><td style="text-align: center">56284</td><td style="text-align: center">42</td><td style="text-align: center">5.03</td><td style="text-align: center">5.26</td><td style="text-align: center">0.189</td><td style="text-align: center">0.199</td><td style="text-align: center">0.612</td><td style="text-align: center">0.801</td><td style="text-align: center">0.047</td><td style="text-align: center">0.159</td><td style="text-align: center">0.294</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">Low</td><td style="text-align: center">221189</td><td style="text-align: center">157</td><td style="text-align: center">19.76</td><td style="text-align: center">19.65</td><td style="text-align: center">0.180</td><td style="text-align: center">0.199</td><td style="text-align: center">0.621</td><td style="text-align: center">0.801</td><td style="text-align: center">-0.007</td><td style="text-align: center">0.089</td><td style="text-align: center">-0.080</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">Moderate</td><td style="text-align: center">426456</td><td style="text-align: center">272</td><td style="text-align: center">38.10</td><td style="text-align: center">34.04</td><td style="text-align: center">0.162</td><td style="text-align: center">0.212</td><td style="text-align: center">0.626</td><td style="text-align: center">0.788</td><td style="text-align: center">-0.176</td><td style="text-align: center">0.075</td><td style="text-align: center">-2.360</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">High</td><td style="text-align: center">328260</td><td style="text-align: center">227</td><td style="text-align: center">29.33</td><td style="text-align: center">28.41</td><td style="text-align: center">0.175</td><td style="text-align: center">0.202</td><td style="text-align: center">0.623</td><td style="text-align: center">0.798</td><td style="text-align: center">-0.045</td><td style="text-align: center">0.078</td><td style="text-align: center">-0.570</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">Very high</td><td style="text-align: center">87020</td><td style="text-align: center">101</td><td style="text-align: center">7.78</td><td style="text-align: center">12.64</td><td style="text-align: center">0.294</td><td style="text-align: center">0.189</td><td style="text-align: center">0.517</td><td style="text-align: center">0.811</td><td style="text-align: center">0.540</td><td style="text-align: center">0.107</td><td style="text-align: center">5.071</td></tr><tr><td style="text-align: center">Slope</td><td style="text-align: center">>10</td><td style="text-align: center">519673</td><td style="text-align: center">291</td><td style="text-align: center">46.43</td><td style="text-align: center">36.42</td><td style="text-align: center">0.014</td><td style="text-align: center">0.194</td><td style="text-align: center">0.792</td><td style="text-align: center">0.806</td><td style="text-align: center">-0.414</td><td style="text-align: center">0.074</td><td style="text-align: center">-5.632</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">10_20</td><td style="text-align: center">470256</td><td style="text-align: center">305</td><td style="text-align: center">42.02</td><td style="text-align: center">38.17</td><td style="text-align: center">0.016</td><td style="text-align: center">0.175</td><td style="text-align: center">0.809</td><td style="text-align: center">0.825</td><td style="text-align: center">-0.160</td><td style="text-align: center">0.073</td><td style="text-align: center">-2.198</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">20_30</td><td style="text-align: center">112814</td><td style="text-align: center">137</td><td style="text-align: center">10.08</td><td style="text-align: center">17.15</td><td style="text-align: center">0.030</td><td style="text-align: center">0.151</td><td style="text-align: center">0.819</td><td style="text-align: center">0.849</td><td style="text-align: center">0.613</td><td style="text-align: center">0.094</td><td style="text-align: center">6.528</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">30_40</td><td style="text-align: center">15083</td><td style="text-align: center">39</td><td style="text-align: center">1.35</td><td style="text-align: center">4.88</td><td style="text-align: center">0.064</td><td style="text-align: center">0.158</td><td style="text-align: center">0.778</td><td style="text-align: center">0.842</td><td style="text-align: center">1.323</td><td style="text-align: center">0.164</td><td style="text-align: center">8.051</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">40_50</td><td style="text-align: center">1320</td><td style="text-align: center">26</td><td style="text-align: center">0.12</td><td style="text-align: center">3.25</td><td style="text-align: center">0.485</td><td style="text-align: center">0.159</td><td style="text-align: center">0.356</td><td style="text-align: center">0.841</td><td style="text-align: center">3.349</td><td style="text-align: center">0.201</td><td style="text-align: center">16.640</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">>50</td><td style="text-align: center">63</td><td style="text-align: center">1</td><td style="text-align: center">0.01</td><td style="text-align: center">0.13</td><td style="text-align: center">0.391</td><td style="text-align: center">0.164</td><td style="text-align: center">0.445</td><td style="text-align: center">0.836</td><td style="text-align: center">3.103</td><td style="text-align: center">1.009</td><td style="text-align: center">3.077</td></tr><tr><td style="text-align: center">Drainage</td><td style="text-align: center">>500</td><td style="text-align: center">103202</td><td style="text-align: center">32</td><td style="text-align: center">9.22</td><td style="text-align: center">4.01</td><td style="text-align: center">0.081</td><td style="text-align: center">0.177</td><td style="text-align: center">0.742</td><td style="text-align: center">0.823</td><td style="text-align: center">-0.890</td><td style="text-align: center">0.180</td><td style="text-align: center">-4.931</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">400_500</td><td style="text-align: center">105408</td><td style="text-align: center">65</td><td style="text-align: center">9.42</td><td style="text-align: center">8.14</td><td style="text-align: center">0.161</td><td style="text-align: center">0.170</td><td style="text-align: center">0.669</td><td style="text-align: center">0.830</td><td style="text-align: center">-0.160</td><td style="text-align: center">0.129</td><td style="text-align: center">-1.240</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">300_400</td><td style="text-align: center">153319</td><td style="text-align: center">80</td><td style="text-align: center">13.70</td><td style="text-align: center">10.01</td><td style="text-align: center">0.136</td><td style="text-align: center">0.175</td><td style="text-align: center">0.689</td><td style="text-align: center">0.825</td><td style="text-align: center">-0.355</td><td style="text-align: center">0.118</td><td style="text-align: center">-3.014</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">200_300</td><td style="text-align: center">193542</td><td style="text-align: center">108</td><td style="text-align: center">17.29</td><td style="text-align: center">13.52</td><td style="text-align: center">0.146</td><td style="text-align: center">0.175</td><td style="text-align: center">0.679</td><td style="text-align: center">0.825</td><td style="text-align: center">-0.291</td><td style="text-align: center">0.104</td><td style="text-align: center">-2.811</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">100_200</td><td style="text-align: center">246063</td><td style="text-align: center">222</td><td style="text-align: center">21.99</td><td style="text-align: center">27.78</td><td style="text-align: center">0.236</td><td style="text-align: center">0.155</td><td style="text-align: center">0.609</td><td style="text-align: center">0.845</td><td style="text-align: center">0.311</td><td style="text-align: center">0.079</td><td style="text-align: center">3.941</td></tr><tr><td style="text-align: center"></td><td style="text-align: center">>100</td><td style="text-align: center">317675</td><td style="text-align: center">292</td><td style="text-align: center">28.38</td><td style="text-align: center">36.55</td><td style="text-align: center">0.240</td><td style="text-align: center">0.148</td><td style="text-align: center">0.612</td><td style="text-align: center">0.852</td><td style="text-align: center">0.374</td><td style="text-align: center">0.073</td><td style="text-align: center">5.085</td></tr></tbody></table><div class="table-wrap-foot"><ol class="footnote-group" id=""><li class="footnote footnote-noLabel" id=""><p>N: number, Bel : belief function, Dis : disbelief function, Pis: Plausibility, C: contrast and S(C): variance of contrast.</p></li></ol></div></div><p>The highest susceptible classes of the aspect is SW followed by E. Also the S and SE classes have an effect on landslide triggering but less than SW and E classes.</p><p>For the elevation factor, the highest weight values are for the 600-800 m asl class followed by the <200 m asl class for the EBF model. But, for the WoE model it is the reverse, the highest weight is for the <200 m asl class followed by the 600-800 m asl class.</p><p>The most susceptible classes of the distance to fault factor is the 1000-2000m class followed by the <1000m class and the 2000-3000m class.</p><p>The clay/sand lithological units are the most susceptible class followed by the clay/marl units for all the two models.</p><p>The land cover/use factor shows that the built up is the most susceptible classes followed by the cultivated area and the bare soil classes.</p><p>Concerning the NDVI factor, the most susceptible class is the low class followed by the very low class. The NDVI low class have the highest value of all classes of all factors.</p><p>For the plan curvature and the profile curvature factors the highest values are for the very low and the very high classes.</p><p>Regarding the rainfall factor, as expected, the most susceptible class is the>1200 mm/year class followed by the 1100-1200 mm/year class, the landslide density increase as the rainfall increase.</p><p>With regard to the slope factor, the highest weights are for the 40-50<sup>∘</sup> and >50<sup>∘</sup> classes (they have similar weights) followed by the 30-40<sup>∘</sup> class for the EBF model. For the WoE model the highest weight is for the 40-50<sup>∘</sup> class followed by 30-40<sup>∘</sup> and >50<sup>∘</sup> classes, respectively.</p><p>Finally, the most susceptible class for the distance to drainage factor is the <100m class and the weights of classes decrease by moving away from the drainage network</p></section><section id="j_geo-2019-0056_s_005_s_002_w2aab3b8d108b1b7b1ab1b4b3Aa"><h3 class="subheading">5.2 Application of statistical models</h3><p>The LSI values range between 1.03 and 3.6 for EBF model, and between −54.72 and 90.56 for the WoE model. The lower the LSI pixel value the less the pixel is susceptible to landslide. The output landslide susceptibility map (LSM) was produced and classified into five classes using the natural breaks (Jenks) method: very low, low, moderate, high and very high for the two models (<a href="#j_geo-2019-0056_fig_013_w2aab3b8d108b1b7b1ab1b4b3b3Aa" class="link link-fig" data-bs-target="j_geo-2019-0056_fig_013_w2aab3b8d108b1b7b1ab1b4b3b3Aa">Figure 3</a>).</p><div class="figure-wrapper" id="j_geo-2019-0056_fig_013_w2aab3b8d108b1b7b1ab1b4b3b3Aa"><div class="figure w-100"><div class="graphic"><img loading="lazy" src="/document/doi/10.1515/geo-2019-0056/asset/graphic/j_geo-2019-0056_fig_013.jpg" alt="Figure 3 LSMs of the EBF (a) and WoE (b) models."></img></div><div class="figure-description mb-3"><div class="figure-label h3"><span class="label">Figure 3</span></div><div class="figure-caption mb-2"><span class="caption"><p>LSMs of the EBF (a) and WoE (b) models.</p></span></div></div></div></div><p>In the current study, the area percentage of each class is shown in (<a href="#j_geo-2019-0056_tab_002_w2aab3b8d108b1b7b1ab1b4b3b5Aa" class="link link-table" data-bs-target="j_geo-2019-0056_tab_002_w2aab3b8d108b1b7b1ab1b4b3b5Aa">Table 2</a>). In the case of the EBF model, the distribution of class area was as following: 15.77% for the very low class, 33.25% for the low class, 32.4% for the moderate class, 13.96% for the high class and 4.62%for the very high class. As regards to the WoE model, the very low, low, moderate, high and very high classes has 18.96%, 33.82%, 28.83%, 12.83% and 5.56% of the entire study area, respectively. Result shows that the spatial distribution of the susceptibility is very similar.</p><div class="table-wrap mb-4" id="j_geo-2019-0056_tab_002_w2aab3b8d108b1b7b1ab1b4b3b5Aa" position="float"><div class="table-label h3">Table 2</div><div class="caption mb-3"><p>Distribution of class area and landslide using natural breaks method</p></div><table xmlns:env="http://degruyter.com/resources/metadata" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dgror="http://degruyter.com/resources/fetched-ror-id" xmlns:m="http://degruyter.com/resources/metadata" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:tei="http://www.tei-c.org/ns/1.0" rules="groups" frame="hsides" class="content-table"><thead><tr><th style="text-align: center">Susceptibility</th><th style="text-align: center" colspan="2">EBF</th><th style="text-align: center" colspan="2">WoE</th></tr><tr><th style="text-align: center"></th><th style="text-align: center">Area (%)</th><th style="text-align: center">Landslide (%)</th><th style="text-align: center">Area (%)</th><th style="text-align: center">Landslide (%)</th></tr></thead><tbody><tr><td style="text-align: center"><strong>very low</strong></td><td style="text-align: center">15.77</td><td style="text-align: center">1.00</td><td style="text-align: center">18.96</td><td style="text-align: center">0.73</td></tr><tr><td style="text-align: center"><strong>low</strong></td><td style="text-align: center">33.25</td><td style="text-align: center">5.18</td><td style="text-align: center">33.82</td><td style="text-align: center">4.55</td></tr><tr><td style="text-align: center"><strong>moderate</strong></td><td style="text-align: center">32.40</td><td style="text-align: center">18.45</td><td style="text-align: center">28.83</td><td style="text-align: center">14.27</td></tr><tr><td style="text-align: center"><strong>high</strong></td><td style="text-align: center">13.96</td><td style="text-align: center">22.00</td><td style="text-align: center">12.83</td><td style="text-align: center">25.09</td></tr><tr><td style="text-align: center"><strong>very high</strong></td><td style="text-align: center">4.62</td><td style="text-align: center">53.36</td><td style="text-align: center">5.56</td><td style="text-align: center">55.36</td></tr></tbody></table></div></section><section id="j_geo-2019-0056_s_005_s_003_w2aab3b8d108b1b7b1ab1b4b4Aa"><h3 class="subheading">5.3 Validation of models</h3><p>The validation and the check of the capabilities of the LSM produced by the two models were carried out with both success and prediction rate curves. ROC curves were plotted by comparing the LSM with the training and the validating data set of the inventory map and the area under the ROC curves was calculated. Result shows that the AUC of the success rate curves were 0.879 for the EBF model and 0.895 for the WoE model (<a href="#j_geo-2019-0056_fig_014_w2aab3b8d108b1b7b1ab1b4b4b3Aa" class="link link-fig" data-bs-target="j_geo-2019-0056_fig_014_w2aab3b8d108b1b7b1ab1b4b4b3Aa">Figure 4a).</a> The AUC of the prediction rate curves were 0.848 for the EBF model and 0.865 for the WoE model (<a href="#j_geo-2019-0056_fig_014_w2aab3b8d108b1b7b1ab1b4b4b3Aa" class="link link-fig" data-bs-target="j_geo-2019-0056_fig_014_w2aab3b8d108b1b7b1ab1b4b4b3Aa">Figure 4b).</a> The AUC of the success rate and predictive rate curves range between 0.8-0.9 indicating a good performance of the two models [<a href="#j_geo-2019-0056_ref_084_w2aab3b8d108b1b7b1ab2ac84Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_084_w2aab3b8d108b1b7b1ab2ac84Aa" data-bs-toggle="tooltip" title="[84] Yesilnacar E, Topal T. Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engineering Geology 2005;79:251-66.10.1016/j.enggeo.2005.02.002Search in Google Scholar">84</a>].</p><div class="figure-wrapper" id="j_geo-2019-0056_fig_014_w2aab3b8d108b1b7b1ab1b4b4b3Aa"><div class="figure w-100"><div class="graphic"><img loading="lazy" src="/document/doi/10.1515/geo-2019-0056/asset/graphic/j_geo-2019-0056_fig_014.jpg" alt="Figure 4 Success and predictive ROC curves (a) EBF and (b) WoE"></img></div><div class="figure-description mb-3"><div class="figure-label h3"><span class="label">Figure 4</span></div><div class="figure-caption mb-2"><span class="caption"><p>Success and predictive ROC curves (a) EBF and (b) WoE</p></span></div></div></div></div></section></section><section id="j_geo-2019-0056_s_006_w2aab3b8d108b1b7b1ab1b5Aa"><h2 class="subheading">6 Discussion</h2><p>In landslide susceptibility bivariate statistics-based method, the preparation of data is very important. Especially, landslide inventory map since all statistics are based on quantities and landslide distribution in the study area. The relationship between conditioning factors and landslide releases is also very important. Based on EBF and WoE as two bivariate statistics method, the weights of all classes of all conditioning factors maps were calculated to reveal the relationship between landslide and every conditioning factor for the present study area. Results show that the susceptibility of each class is similar by the two models indicating that if a factor class is susceptible for landslide, it must have a high weight for any statistical method [<a href="#j_geo-2019-0056_ref_028_w2aab3b8d108b1b7b1ab2ac28Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_028_w2aab3b8d108b1b7b1ab2ac28Aa" data-bs-toggle="tooltip" title="[28] Vakhshoori V, Zare M. Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods. Geomatics, Natural Hazards and Risk 2016;7:1731-52.10.1080/19475705.2016.1144655Search in Google Scholar">28</a>].</p><p>In the present study, results show that the most susceptible class of the aspect factor was the SW followed by the E, S and SE classes. This may be due to the dry and warm summer wind coming from the S and/or the SE Tunisian prevailing wind. In summers, these slopes are exposed to warm wind, therefore clay lithological units shrink and drying slots appear which facilitates the wind and rainfall infiltration. This process leads to a deep and quick alteration of clay units which become more prone to landslide.</p><p>For the elevation there was no specific correlation between the altitude and landslide. The most susceptible class was 600-800m asl which has a medium elevation in the study area. Many researchers reveal that susceptibility is low for higher elevation due to the presence of bedrocks resistant to weathering processes [<a href="#j_geo-2019-0056_ref_019_w2aab3b8d108b1b7b1ab2ac19Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_019_w2aab3b8d108b1b7b1ab2ac19Aa" data-bs-toggle="tooltip" title="[19] Zare M, Pourghasemi HR, Vafakhah M, Pradhan B. Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arabian Journal of Geosciences 2013;6:2873-88.10.1007/s12517-012-0610-xSearch in Google Scholar">19</a>, <a href="#j_geo-2019-0056_ref_055_w2aab3b8d108b1b7b1ab2ac55Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_055_w2aab3b8d108b1b7b1ab2ac55Aa" data-bs-toggle="tooltip" title="[55] Pourghasemi HR, Pradhan B, Gokceoglu C. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural hazards 2012;63:965-96.10.1007/s11069-012-0217-2Search in Google Scholar">55</a>]. The high weight of the <200m asl class is due to the fact that low elevation accumulate loosely consolidated components of erosion scraps and screeds [<a href="#j_geo-2019-0056_ref_085_w2aab3b8d108b1b7b1ab2ac85Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_085_w2aab3b8d108b1b7b1ab2ac85Aa" data-bs-toggle="tooltip" title="[85] Hadji R, Rais K, Gadri L, Chouabi A, Hamed Y. Slope Failure Characteristics and Slope Movement Susceptibility Assessment Using GIS in a Medium Scale: A Case Study from Ouled Driss and Machroha Municipalities, Northeast Algeria. Arabian Journal for Science and Engineering 2016:1-20.10.1007/s13369-016-2046-1Search in Google Scholar">85</a>].</p><p>Concerning the linear distance to fault factor there is no clear relationship with landslide, this may be due to the infrequent tectonic activity in the study area.</p><p>With regards to the relationship between landslide and lithological units, the most susceptible class was the clay/sand units followed by the clay/marl units which indicate the effect of clay on landslide triggering. The alternation of sand with clay beds may increase the susceptibility to landslide by accumulating the rainfalls water for long times which decrease the shear strength of clay beds. Also, the presence of sand as loose material in slopes can come in as a sliding surface during rainfall.</p><p>Regarding the plan curvature and profile curvature factors, the susceptible classes were the extreme classes (concave and convex), which is logical because the increase of slope convexity increase the landslide susceptibility; also concavity and convexity are two mutual parameters.</p><p>Classes with high precipitation of rainfall factor were more susceptible. Indeed, rainfall increase the water content of clay formation which increase the pore pressure and decrease the shear strength of clay units [<a href="#j_geo-2019-0056_ref_069_w2aab3b8d108b1b7b1ab2ac69Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_069_w2aab3b8d108b1b7b1ab2ac69Aa" data-bs-toggle="tooltip" title="[69] Yalcin A. The effects of clay on landslides: A case study. Applied Clay Science 2007;38:77-85.10.1016/j.clay.2007.01.007Search in Google Scholar">69</a>]. Also water play as lubricant of clay minerals which facilitates their sliding [<a href="#j_geo-2019-0056_ref_086_w2aab3b8d108b1b7b1ab2ac86Aa" class="link link-bibr" data-bs-target="j_geo-2019-0056_ref_086_w2aab3b8d108b1b7b1ab2ac86Aa" data-bs-toggle="tooltip" title="[86] Horn H, Deere D. Frictional characteristics of minerals. Geotechnique 1962;12:319-35.10.1680/geot.1962.12.4.319Search in Google Scholar">86</a>].</p><p>As expected the high slope angle classes were more susceptible, the 40<sup>∘</sup>-50<sup>∘</sup> class was the most susceptible followed by the >50<sup>∘</sup> class due to the small area of the >50<sup>∘</sup> class (0.01% of the study area). For the WoE model, the 30<sup>∘</sup>-40<sup>∘</sup> class was more susceptible than the >50<sup>∘</sup> class owing to the high variance of this class (one landslide pixel). Generally, landslide susceptibility increase as the slope angle increase on account of the increasing of shear stress of soil.</p><p>Concerning the linear distance to drainage network, the landslide susceptibility increases inversely proportional to the distance. Drainage networks accumulate the erosion remains which are loose material. Also, the drainage networks increase the water content of adjacent soils by accumulating rainfalls water.</p><p>For the land cover/use, the most susceptible class was the built up class followed by the cultivated area and bare soil classes. The forest class is the least susceptible class by dint of tree roots which fixes the soil, this is why bare soils were more susceptible than forest. The susceptibility of the cultivated area class can be attributed to the irrigation and the very loose soil in slope. The very high susceptibility of the built up class is due to the disruption of natural slope by the house building and especially the road construction in slope area without strong geotechnical studies. This was in line with the NDVI classes weights. In fact, the low class of NDVI factor was the most susceptiblewhich can be attributed to the buildings (constructions and roads) because the very low class may attributed to water accumulation in rivers.</p><p>In this study, two LSMs were established using EBF and WoE as bivariate statistical models. Results show a very good accuracy of the EBF and WoE models. The WoE success rate and predictive rate are more than the EBF model indicating that the WoE model can be more efficient than the EBF model for the current study.</p></section><section id="j_geo-2019-0056_s_007_w2aab3b8d108b1b7b1ab1b6Aa"><h2 class="subheading">7 Conclusion</h2><p>The Tabarka/ Ain-Drahim region in the Northwestern area of Tunisia present several landslides every year which cause damages to infrastructures and properties. In this study, 11 conditioning factors were prepared: aspect, elevation, rainfall, lithology, slope, distance to drainage network, distance to fault, plan curvature, profile curvature, NDVI and land cover/use. Using aerial photo and extent field investigation, an inventory map of landslides, that have occurred since 2004, was produced and 451 landslides have been located. A randomly selection of 316 landslides, which represent 70% of all landslides, were used to produce landslide susceptibility models and 135 landslides (30%) were used to validate models.</p><p>The statistical relationship between conditioning factors and landslides was studied using the inventory map. The low NDVI class (judged as buildings) and the built up land cover/use class had the highest weights. The anthropogenic factor by the disturbance of natural slope is the main cause of landslides in the study area.</p><p>A GIS-based EBF and WoE bivariate statistical models were applied. In order to check and validate the capabilities of models both success and predictive rates using AUROC curve were calculated. The success rates and predictive rates of the two models were about 90% showing a good performance of models and good capabilities in predicting future landslides for the current study area.</p><p>The landslide susceptibility map of the WoE model was deemed to be the best map and it may be useful in the future especially in geotechnical planning to help avoiding the existing mistakes.</p></section></div><div class="contrib-group"></div><div class="back"><span class="ref-list"><h2 class="subheading">References</h2><p class="reference" id="j_geo-2019-0056_ref_001_w2aab3b8d108b1b7b1ab2ab1Aa"><span class="reference-label d-inlineblock me-4">[1] </span><span class="reference-mixed-citation">Del Ventisette C, Garfagnoli F, Ciampalini A, et al. Catastrophic debris-flows: geological hazard and human influence. 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"GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia" <i>Open Geosciences</i>, vol. 11, no. 1, 2019, pp. 708-726. <a href='https://doi.org/10.1515/geo-2019-0056'>https://doi.org/10.1515/geo-2019-0056</a></div> <div class="tab-pane fade " id="APA" role="tabpanel" aria-labelledby="APA-tab">Anis, Z., Wissem, G., Vali, V., Smida, H. & Mohamed Essghaier, G. (2019). GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia. <i>Open Geosciences</i>, <i>11</i>(1), 708-726. <a href='https://doi.org/10.1515/geo-2019-0056'>https://doi.org/10.1515/geo-2019-0056</a></div> <div class="tab-pane fade " id="Harvard" role="tabpanel" aria-labelledby="Harvard-tab">Anis, Z., Wissem, G., Vali, V., Smida, H. and Mohamed Essghaier, G. (2019) GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia. Open Geosciences, Vol. 11 (Issue 1), pp. 708-726. <a href='https://doi.org/10.1515/geo-2019-0056'>https://doi.org/10.1515/geo-2019-0056</a></div> <div class="tab-pane fade " id="Chicago" role="tabpanel" aria-labelledby="Chicago-tab">Anis, Zorgati, Wissem, Gallala, Vali, Vakhshoori, Smida, Habib and Mohamed Essghaier, Gaied. "GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia" <i>Open Geosciences</i> 11, no. 1 (2019): 708-726. <a href='https://doi.org/10.1515/geo-2019-0056'>https://doi.org/10.1515/geo-2019-0056</a></div> <div class="tab-pane fade " id="Vancouver" role="tabpanel" aria-labelledby="Vancouver-tab">Anis Z, Wissem G, Vali V, Smida H, Mohamed Essghaier G. GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia. <i>Open Geosciences</i>. 2019;11(1): 708-726. <a href='https://doi.org/10.1515/geo-2019-0056'>https://doi.org/10.1515/geo-2019-0056</a></div> <div class="d-flex justify-content-center"><span id="citationCopied" aria-hidden="true">Copied to clipboard</span></div> </div> <div class="d-flex justify-content-between mt-2"> <div><a href="#" id="citationCopy" class="font-size-14">Copy to clipboard</a></div> <div> <span class="font-size-14">Download:</span> <a title='Download in BibTeX format' aria-label='Download in BibTeX format' class="ms-1 font-size-14" href="/document/doi/10.1515/geo-2019-0056/machineReadableCitation/BibTeX">BibTeX</a> <a title='Download in EndNote format' aria-label='Download in EndNote format' class="ms-1 font-size-14" href="/document/doi/10.1515/geo-2019-0056/machineReadableCitation/EndNote">EndNote</a> <a title='Download in RIS format' aria-label='Download in RIS 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GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia