Enhancing Spatial Interpolation: A Multi-Layer Inverse Distance Weighting Model for Complex Regression and Classification Tasks in Spatial Data Analysis

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A Multi-Layer Inverse Distance Weighting Model for Complex Regression and Classification Tasks in Spatial Data Analysis</title> <meta name="description" content="Enhancing Spatial Interpolation: A Multi-Layer Inverse Distance Weighting Model for Complex Regression and Classification Tasks in Spatial Data Analysis"> <meta name="keywords" content="Deep Learning, Multi-Layer Neural Networks, Gradient Descent, Spatial Interpolation, Inverse Distance Weighting."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Enhancing Spatial Interpolation: A Multi-Layer Inverse Distance Weighting Model for Complex Regression and Classification Tasks in Spatial Data Analysis"> <meta name="citation_author" content="Yakin Hajlaoui"> <meta name="citation_author" content="Richard Labib"> <meta name="citation_author" content="Jean-Franc¸ois Plante"> <meta name="citation_author" content="Michel Gamache"> <meta name="citation_publication_date" content="2024/10/23"> <meta name="citation_journal_title" content="International Journal of Mathematical and Computational Sciences"> <meta name="citation_volume" content="18"> <meta name="citation_issue" content="10"> <meta name="citation_firstpage" content="121"> <meta name="citation_lastpage" content="128"> <meta name="citation_pdf_url" content="https://publications.waset.org/10013861/pdf"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img 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mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Enhancing Spatial Interpolation: A Multi-Layer Inverse Distance Weighting Model for Complex Regression and Classification Tasks in Spatial Data Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Yakin%20Hajlaoui">Yakin Hajlaoui</a>, <a href="https://publications.waset.org/search?q=Richard%20Labib"> Richard Labib</a>, <a href="https://publications.waset.org/search?q=Jean-Franc%C2%B8ois%20Plante"> Jean-Franc¸ois Plante</a>, <a href="https://publications.waset.org/search?q=Michel%20Gamache"> Michel Gamache</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This study presents the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs’ processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW’s ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. We employ gradient descent and backpropagation to train ML-IDW. The performance of the proposed model is compared against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. Our results highlight the efficacy of ML-IDW, particularly in handling complex spatial dataset, exhibiting lower mean square error in regression and higher F1 score in classification.</p> <iframe src="https://publications.waset.org/10013861.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Deep%20Learning" title="Deep Learning">Deep Learning</a>, <a href="https://publications.waset.org/search?q=Multi-Layer%20Neural%20Networks" title=" Multi-Layer Neural Networks"> Multi-Layer Neural Networks</a>, <a href="https://publications.waset.org/search?q=Gradient%20Descent" title=" Gradient Descent"> Gradient Descent</a>, <a href="https://publications.waset.org/search?q=Spatial%20Interpolation" title=" Spatial Interpolation"> Spatial Interpolation</a>, <a href="https://publications.waset.org/search?q=Inverse%20Distance%20Weighting." title=" Inverse Distance Weighting."> Inverse Distance Weighting.</a> </p> <a href="https://publications.waset.org/10013861/enhancing-spatial-interpolation-a-multi-layer-inverse-distance-weighting-model-for-complex-regression-and-classification-tasks-in-spatial-data-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013861/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013861/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013861/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013861/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013861/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013861/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013861/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013861/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013861/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013861/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013861.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">34</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] J. Heaton, “Ian goodfellow, yoshua bengio, and aaron courville: Deep learning: The mit press, 2016, 800 pp, isbn: 0262035618,” Genetic programming and evolvable machines, vol. 19, no. 1, pp. 305–307, 2018. <br>[2] C. M. Bishop and N. M. Nasrabadi, Pattern recognition and machine learning. Springer, 2006, vol. 4, no. 4. <br>[3] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” nature, vol. 323, no. 6088, pp. 533–536, 1986. <br>[4] J. Kim, Y. Lee, M.-H. Lee, and S.-Y. Hong, “A comparative study of machine learning and spatial interpolation methods for predicting house prices,” Sustainability, vol. 14, no. 15, p. 9056, 2022. <br>[5] I. Chahrour and J.Wells, “Comparing machine learning and interpolation methods for loop-level calculations,” SciPost Physics, vol. 12, no. 6, p. 187, 2022. <br>[6] D. Radoˇcaj, M. Juriˇsi´c, R. ˇ Zupan, and O. 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Enhancing Spatial Interpolation: A Multi-Layer Inverse Distance Weighting Model for Complex Regression and Classification Tasks in Spatial Data Analysis