An ensemble random forest tree with SVM, ANN, NBT, and LMT for landslide susceptibility mapping in the Rangit River watershed, India

Sk Ajim Ali, Farhana Parvin, Quoc Bao Pham, Khaled Mohamed Khedher, Mahro Dehbozorgi, Yasin Wahid Rabby, Duong Tran Anh () and Duc Hiep Nguyen
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Sk Ajim Ali: Aligarh Muslim University (AMU)
Farhana Parvin: Aligarh Muslim University (AMU)
Quoc Bao Pham: Thu Dau Mot University
Khaled Mohamed Khedher: King Khalid University
Mahro Dehbozorgi: University of Tehran
Yasin Wahid Rabby: Wake Forest University
Duong Tran Anh: HUTECH University
Duc Hiep Nguyen: Ton Duc Thang University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 113, issue 3, No 9, 1633 pages

Abstract: Abstract This study examined landslide susceptibility, an increasingly common problem in mountainous regions across the world as a result of urbanization, deforestation, and various natural processes. The Rangit River watershed in Sikkim Himalaya is one of the most landslide-prone areas in India. The main objective of this study was to produce landslide susceptibility maps of the Rangit River watershed using novel ensembles of random forest tree (RFT) with support vector machine (RFT-SVM), artificial neural network (RFT-ANN), naïve Bayes tree (RFT-NBT), and logistic model tree (RFT-LMT). An inventory of landslides was created using historical landslide data, government and scientific studies, and Google Earth’s high-resolution satellite images. The landslide/non-landslide locations were split 70/30 for training and validating the models, respectively. Eleven landslide conditioning factors were selected based on their predictive ability, determined using the information gain method, and each factor’s importance was derived. A landslide susceptibility index was then estimated by weighted overlay using a model builder in a GIS (Geographic Information System) environment. Based on the area under the curve and statistical metrics, RFT-LMT was identified as the best model. The results showed that approximately 40% of the Rangit River watershed has high to very high susceptibility to landslides. This study’s findings will be useful for policy-makers and land use planners in managing and mitigating future landslides in the study area.

Keywords: Landslide susceptibility; Rangit River watershed; Random forest tree; Machine learning algorithms; Ensemble models (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11069-022-05360-5

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