Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia

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ID: 119170
2018
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Abstract
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.
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bui2018remotelandslide Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Dieu Tien Bui;Himan Shahabi;Ataollah Shirzadi;Kamran Chapi;Mohsen Alizadeh;Wei Chen;Ayub Mohammadi;Baharin Bin Ahmad;Mahdi Panahi;Haoyuan Hong;Yingying Tian;Tien Bui, Dieu;Shahabi, Himan;Shirzadi, Ataollah;Chapi, Kamran;Alizadeh, Mohsen;Chen, Wei;Mohammadi, Ayub;Ahmad, Baharin Bin;Panahi, Mahdi;Hong, Haoyuan;Tian, Yingying;
Journal remote sensing
Year 2018
DOI
10.3390/rs10101527
URL
https://dx.doi.org/10.3390/rs10101527
Keywords
malaysia landslide susceptibility airsar data optical satellite images gis modeling

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