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Open AccessArticle

A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India

Department of Geography, University of GourBanga, West Bengal 732103, India
Department of Geomorphology, Tarbiat Modares University, Tehran 14115-111, Iran
Department of Geoinformatics – Z_GIS, University of Salzburg, 5020 Salzburg, Austria
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2866;
Received: 12 October 2019 / Revised: 11 November 2019 / Accepted: 15 November 2019 / Published: 2 December 2019
(This article belongs to the Special Issue Remote Sensing of Natural Hazards)
Landslides are among the most harmful natural hazards for human beings. This study aims to delineate landslide hazard zones in the Darjeeling and Kalimpong districts of West Bengal, India using a novel ensemble approach combining the weight-of-evidence (WofE) and support vector machine (SVM) techniques with remote sensing datasets and geographic information systems (GIS). The study area currently faces severe landslide problems, causing fatalities and losses of property. In the present study, the landslide inventory database was prepared using Google Earth imagery, and a field investigation carried out with a global positioning system (GPS). Of the 326 landslides in the inventory, 98 landslides (30%) were used for validation, and 228 landslides (70%) were used for modeling purposes. The landslide conditioning factors of elevation, rainfall, slope, aspect, geomorphology, geology, soil texture, land use/land cover (LULC), normalized differential vegetation index (NDVI), topographic wetness index (TWI), sediment transportation index (STI), stream power index (SPI), and seismic zone maps were used as independent variables in the modeling process. The weight-of-evidence and SVM techniques were ensembled and used to prepare landslide susceptibility maps (LSMs) with the help of remote sensing (RS) data and geographical information systems (GIS). The landslide susceptibility maps (LSMs) were then classified into four classes; namely, low, medium, high, and very high susceptibility to landslide occurrence, using the natural breaks classification methods in the GIS environment. The very high susceptibility zones produced by these ensemble models cover an area of 630 km2 (WofE& RBF-SVM), 474 km2 (WofE& Linear-SVM), 501km2 (WofE& Polynomial-SVM), and 498 km2 (WofE& Sigmoid-SVM), respectively, of a total area of 3914 km2. The results of our study were validated using the receiver operating characteristic (ROC) curve and quality sum (Qs) methods. The area under the curve (AUC) values of the ensemble WofE& RBF-SVM, WofE & Linear-SVM, WofE & Polynomial-SVM, and WofE & Sigmoid-SVM models are 87%, 90%, 88%, and 85%, respectively, which indicates they are very good models for identifying landslide hazard zones. As per the results of both validation methods, the WofE & Linear-SVM model is more accurate than the other ensemble models. The results obtained from this study using our new ensemble methods can provide proper and significant information to decision-makers and policy planners in the landslide-prone areas of these districts. View Full-Text
Keywords: landslide; machine learning models; remote sensing; ensemble models; validation landslide; machine learning models; remote sensing; ensemble models; validation
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MDPI and ACS Style

Roy, J.; Saha, S.; Arabameri, A.; Blaschke, T.; Bui, D.T. A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India. Remote Sens. 2019, 11, 2866.

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