{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T02:52:38Z","timestamp":1772765558150,"version":"3.50.1"},"reference-count":118,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,9,23]],"date-time":"2018-09-23T00:00:00Z","timestamp":1537660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universiti Teknologi Malaysia (UTM)","award":["Q.J130000.2527.17H84"],"award-info":[{"award-number":["Q.J130000.2527.17H84"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Since landslide detection using the combination of AIRSAR data and GIS-based susceptibility mapping has been rarely conducted in tropical environments, the aim of this study is to compare and validate support vector machine (SVM) and index of entropy (IOE) methods for landslide susceptibility assessment in Cameron Highlands area, Malaysia. For this purpose, ten conditioning factors and observed landslides were detected by AIRSAR data, WorldView-1 and SPOT 5 satellite images. A spatial database was generated including a total of 92 landslide locations encompassing the same number of observed and detected landslides, which was divided into training (80%; 74 landslide locations) and validation (20%; 18 landslide locations) datasets. Results of the difference between observed and detected landslides using root mean square error (RMSE) indicated that only 16.3% error exists, which is fairly acceptable. The validation process was performed using statistical-based measures and the area under the receiver operating characteristic (AUROC) curves. Results of validation process indicated that the SVM model has the highest values of sensitivity (88.9%), specificity (77.8%), accuracy (83.3%), Kappa (0.663) and AUROC (84.5%), followed by the IOE model. Overall, the SVM model applied to detected landslides is considered to be a promising technique that could be tested and utilized for landslide susceptibility assessment in tropical environments.<\/jats:p>","DOI":"10.3390\/rs10101527","type":"journal-article","created":{"date-parts":[[2018,9,24]],"date-time":"2018-09-24T10:38:49Z","timestamp":1537785529000},"page":"1527","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":143,"title":["Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5161-6479","authenticated-orcid":false,"given":"Dieu","family":"Tien Bui","sequence":"first","affiliation":[{"name":"Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam"},{"name":"Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5091-6947","authenticated-orcid":false,"given":"Himan","family":"Shahabi","sequence":"additional","affiliation":[{"name":"Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9668-8687","authenticated-orcid":false,"given":"Ataollah","family":"Shirzadi","sequence":"additional","affiliation":[{"name":"Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9466-665X","authenticated-orcid":false,"given":"Kamran","family":"Chapi","sequence":"additional","affiliation":[{"name":"Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran"}]},{"given":"Mohsen","family":"Alizadeh","sequence":"additional","affiliation":[{"name":"Department of Urban Regional Planning, Faculty of Built Environment, Universiti Teknology Malaysia (UTM), Skudai 81310, Malaysia"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Geology &amp; Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]},{"given":"Ayub","family":"Mohammadi","sequence":"additional","affiliation":[{"name":"Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia (UTM), Skudai 81310, Malaysia"}]},{"given":"Baharin","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia (UTM), Skudai 81310, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7601-9208","authenticated-orcid":false,"given":"Mahdi","family":"Panahi","sequence":"additional","affiliation":[{"name":"Young Researchers and Elites Club, North Tehran Branch, Islamic Azad University, Tehran P.O. Box 19585\/466, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6224-069X","authenticated-orcid":false,"given":"Haoyuan","family":"Hong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Nanjing 210023, China"},{"name":"State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China"}]},{"given":"Yingying","family":"Tian","sequence":"additional","affiliation":[{"name":"Key Laboratory of Active Tectonics and Volcano, Institute of Geology, China Earthquake Administration, Beijing 100029, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1007\/s12665-009-0394-9","article-title":"Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: Conditional probability, logistic regression, artificial neural networks, and support vector machine","volume":"61","author":"Yilmaz","year":"2010","journal-title":"Environ. 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