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<!DOCTYPE html> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <title>FFO: Forest Fire Ontology and Reasoning System for Enhanced Alert and Management Services</title>  <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta http-equiv="X-UA-Compatible" content="ie=edge"> <meta name="title" content="FFO: Forest Fire Ontology and Reasoning System for Enhanced Alert and Management Services"> <meta name="description" content="Forest fires or wildfires pose a serious threat to property, lives, and the environment. Early detection and mitigation of such emergencies, therefore, play an important role in reducing the severity of the impact caused by wildfire. Unfortunately, there is often an improper or delayed mechanism for forest fire detection which leads to destruction and losses. These anomalies in detection can be due to defects in sensors or a lack of proper information interoperability among the sensors deployed in forests. This paper presents a lightweight ontological framework to address these challenges. Interoperability issues are caused due to heterogeneity in technologies used and heterogeneous data created by different sensors. Therefore, through the proposed Forest Fire Detection and Management Ontology (FFO), we introduce a standardized model to share and reuse knowledge and data across different sensors. The proposed ontology is validated using semantic reasoning and query processing. The reasoning and querying processes are performed on real-time data gathered from experiments conducted in a forest and stored as RDF triples based on the design of the ontology. The outcomes of queries and inferences from reasoning demonstrate that FFO is feasible for the early detection of wildfire and facilitates efficient process management subsequent to detection."/> <meta name="keywords" content="Semantic Web, Ontology, Semantic Reasoning, Query Processing, SSN,SPARQL, RDF, Geosparql, SWRL, Forest Fire"/>   <meta name="dc.title" content="FFO: Forest Fire Ontology and Reasoning System for Enhanced Alert and Management Services "> <meta name="citation_authors" content="Shibani Das"> <meta name="citation_authors" content="Abhishek Srivastava"> <meta name="citation_authors" content="Indian Institute of Technology Indore, "> <meta name="citation_authors" content="India"> <meta name="dc.type" content="Article"> <meta name="dc.source" content="Computer Science & Information Technology (CS & IT), Vol.14, No.6"> <meta name="dc.date" content="2024/03/30"> <meta name="dc.identifier" content="10.5121/csit.2024.140601 "> <meta name="dc.publisher" content="AIRCC Publishing Corporation"> <meta name="dc.rights" content="http://creativecommons.org/licenses/by/3.0/"> <meta name="dc.format" content="application/pdf"> <meta name="dc.language" content="en"> <meta name="dc.description" content="Forest fires or wildfires pose a serious threat to property, lives, and the environment. Early detection and mitigation of such emergencies, therefore, play an important role in reducing the severity of the impact caused by wildfire. Unfortunately, there is often an improper or delayed mechanism for forest fire detection which leads to destruction and losses. These anomalies in detection can be due to defects in sensors or a lack of proper information interoperability among the sensors deployed in forests. This paper presents a lightweight ontological framework to address these challenges. Interoperability issues are caused due to heterogeneity in technologies used and heterogeneous data created by different sensors. Therefore, through the proposed Forest Fire Detection and Management Ontology (FFO), we introduce a standardized model to share and reuse knowledge and data across different sensors. The proposed ontology is validated using semantic reasoning and query processing. The reasoning and querying processes are performed on real-time data gathered from experiments conducted in a forest and stored as RDF triples based on the design of the ontology. The outcomes of queries and inferences from reasoning demonstrate that FFO is feasible for the early detection of wildfire and facilitates efficient process management subsequent to detection."/> <meta name="dc.subject" content="Semantic Web"> <meta name="dc.subject" content="Ontology"> <meta name="dc.subject" content="Semantic Reasoning"> <meta name="dc.subject" content="Query Processing"> <meta name="dc.subject" content="SSN"> <meta name="dc.subject" content="SPARQL"> <meta name="dc.subject" content="RDF"> <meta name="dc.subject" content="Geosparql"> <meta name="dc.subject" content="SWRL"> <meta name="dc.subject" content="Forest Fire"> <meta name="dc.subject" content="Proceedings"> <meta name="dc.subject" content="Computer Science"> <meta name="dc.subject" content="Technology"> <meta name="dc.subject" content="open access proceedings">   <meta name="prism.publicationName" content="Computer Science & Information Technology (CS & IT)"> <meta name="prism.publicationDate" content="2024/01/30"> <meta name="prism.volume" content="15"> <meta name="prism.number" content="2"> <meta name="prism.section" content="Article"> <meta name="prism.startingPage" content="01">   <meta name="citation_journal_title" content="FFO: Forest Fire Ontology and Reasoning System for Enhanced Alert and Management Services"> <meta name="citation_publisher" content="AIRCC Publishing Corporation"> <meta name="citation_authors" content="Shibani Das and Abhishek Srivastava, Indian Institute of Technology Indore, India"> <meta name="citation_title" content="FFO: Forest Fire Ontology and Reasoning System for Enhanced Alert and Management Services "> <meta name="citation_online_date" content="2024/03/30"> <meta name="citation_issue" content="15"> <meta name="citation_firstpage" content="1"> <meta name="citation_authors" content="Shibani Das "> <meta name="citation_authors" content="Abhishek Srivastavae"> <meta name="citation_authors" content="Indian Institute of Technology Indore"> <meta name="citation_authors" content="India"> <meta name="citation_doi" content="DOI: 10.5121/ijwest.2024.15201 "> <meta name="citation_abstract_html_url" content="https://aircconline.com/abstract/ijwest/v15n2/15224ijwest01.html"> <meta name="citation_pdf_url" content="https://aircconline.com/ijwest/V15N2/15224ijwest01.pdf">   <meta property="og:site_name" content="AIRCC" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://aircconline.com/csit/papers/vol14/csit140601.pdf"> <meta property="og:title" content="FFO: Forest Fire Ontology and Reasoning System for Enhanced Alert and Management Services "> <meta property="og:description" content="Forest fires or wildfires pose a serious threat to property, lives, and the environment. Early detection and mitigation of such emergencies, therefore, play an important role in reducing the severity of the impact caused by wildfire. Unfortunately, there is often an improper or delayed mechanism for forest fire detection which leads to destruction and losses. These anomalies in detection can be due to defects in sensors or a lack of proper information interoperability among the sensors deployed in forests. This paper presents a lightweight ontological framework to address these challenges. Interoperability issues are caused due to heterogeneity in technologies used and heterogeneous data created by different sensors. Therefore, through the proposed Forest Fire Detection and Management Ontology (FFO), we introduce a standardized model to share and reuse knowledge and data across different sensors. The proposed ontology is validated using semantic reasoning and query processing. The reasoning and querying processes are performed on real-time data gathered from experiments conducted in a forest and stored as RDF triples based on the design of the ontology. The outcomes of queries and inferences from reasoning demonstrate that FFO is feasible for the early detection of wildfire and facilitates efficient process management subsequent to detection.."/>   <meta name="twitter:card" content="Proceedings" /> <meta name="twitter:site" content="AIRCC" /> <meta name="twitter:title" content="FFO: Forest Fire Ontology and Reasoning System for Enhanced Alert and Management Services"/> <meta name="twitter:description" content="Forest fires or wildfires pose a serious threat to property, lives, and the environment. Early detection and mitigation of such emergencies, therefore, play an important role in reducing the severity of the impact caused by wildfire. Unfortunately, there is often an improper or delayed mechanism for forest fire detection which leads to destruction and losses. These anomalies in detection can be due to defects in sensors or a lack of proper information interoperability among the sensors deployed in forests. This paper presents a lightweight ontological framework to address these challenges. Interoperability issues are caused due to heterogeneity in technologies used and heterogeneous data created by different sensors. Therefore, through the proposed Forest Fire Detection and Management Ontology (FFO), we introduce a standardized model to share and reuse knowledge and data across different sensors. The proposed ontology is validated using semantic reasoning and query processing. The reasoning and querying processes are performed on real-time data gathered from experiments conducted in a forest and stored as RDF triples based on the design of the ontology. The outcomes of queries and inferences from reasoning demonstrate that FFO is feasible for the early detection of wildfire and facilitates efficient process management subsequent to detection."/> <meta name="twitter:image" content="https://airccse.org/img/aircc-logo1.jpg" />   <meta name="google-site-verification" content="t8rHIcM8EfjIqfQzQ0IdYIiA9JxDD0uUZAitBCzsOIw" /> <meta name="yandex-verification" content="e3d2d5a32c7241f4" />  <style type="text/css"> a{ color:white; text-decoration:none; } ul li a{ font-weight:bold; color:#000; list-style:none; text-decoration:none; size:10px;} .imagess { height:90px; text-align:left; margin:0px 5px 2px 8px; float:right; border:none; } #left p { font-family:CALIBRI; font-size:0.90pc; margin-left: 20px; } .right { margin-right: 20px; } #button{ float: left; font-size: 17px; margin-left: 10px; height: 28px; width: 100px; background-color: #1e86c6; } </style> <link rel="icon" type="image/ico" href="/favicon.ico"/> <link rel="stylesheet" type="text/css" href="../current.css" /> </head> <body> <div id="wap"> <div id="page"> <div id="top"> <table width="100%" cellspacing="0" cellpadding="0" > <tr><td colspan="3" valign="top"><img src="../ijwest.jpg" /></td></tr> </table> </div> <div id="menu"> <a href="http://www.airccse.org/journal/ijwest/ijwest.html">Home</a> <a href="http://www.airccse.org/journal/ijwest/editorialboard.html">Editorial</a> <a href="http://www.airccse.org/journal/ijwest/papersub.html">Submission</a> <a href="http://www.airccse.org/journal/ijwest/indexing.html">Indexing</a> <a href="http://www.airccse.org/journal/ijwest/specialissue.html">Special Issue</a> <a href="http://www.airccse.org/journal/ijwest/contact.html">Contacts</a> <a href="http://airccse.org" target="_blank">AIRCC</a></div> <div id="content"> <div id="left"> <h2>Volume 15, Number 2</h2> <h4 style="text-align:center;"><a>FFO: Forest Fire Ontology and Reasoning System for Enhanced Alert and Management Services</a></h4> <h3>  Authors</h3> <p class="#left">Shibani Das and Abhishek Srivastava, Indian Institute of Technology Indore, India </p> <h3>  Abstract</h3> <p class="#left right" style="text-align:justify">Forest fires or wildfires pose a serious threat to property, lives, and the environment. Early detection and mitigation of such emergencies, therefore, play an important role in reducing the severity of the impact caused by wildfire. Unfortunately, there is often an improper or delayed mechanism for forest fire detection which leads to destruction and losses. These anomalies in detection can be due to defects in sensors or a lack of proper information interoperability among the sensors deployed in forests. This paper presents a lightweight ontological framework to address these challenges. Interoperability issues are caused due to heterogeneity in technologies used and heterogeneous data created by different sensors. Therefore, through the proposed Forest Fire Detection and Management Ontology (FFO), we introduce a standardized model to share and reuse knowledge and data across different sensors. The proposed ontology is validated using semantic reasoning and query processing. The reasoning and querying processes are performed on real-time data gathered from experiments conducted in a forest and stored as RDF triples based on the design of the ontology. The outcomes of queries and inferences from reasoning demonstrate that FFO is feasible for the early detection of wildfire and facilitates efficient process management subsequent to detection. </p> <h3>  Keywords</h3> <p class="#left right" style="text-align:justify">Semantic Web, Ontology, Semantic Reasoning, Query Processing, SSN,SPARQL, RDF, Geosparql, SWRL, Forest Fire. </p> <br> <button type="button" id="button"><a target="blank" href="https://aircconline.com/ijwest/V15N2/15224ijwest01.pdf">Full Text</a></button>   <button type="button" id="button"><a href="http://www.airccse.org/journal/ijwest/vol15.html">Volume 15</a></button> <br><br><br><br><br> </div> <div id="right"> <div class="menu_right"> <ul> <li><a href="http://www.airccse.org/journal/ijwest/archives.html">Archives</a></li> </ul> </div><br /> <p align="center"> </p> <p align="center"> </p> </div> <div class="clear"></div> <div id="footer"><table width="100%" ><tr><td height="25" colspan="2"><br /><p align="center">® All Rights Reserved - AIRCC</p></td></table> </div> </div> </div> </div> </body> </html>