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

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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.

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Authors and Affiliations

  1. Department of Geography, Faculty of Science, Aligarh Muslim University (AMU), Aligarh, UP, 202002, India

    Sk Ajim Ali & Farhana Parvin

  2. Institute of Applied Technology, Thu Dau Mot University, Binh Duong province, Vietnam

    Quoc Bao Pham

  3. Department of Civil Engineering, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia

    Khaled Mohamed Khedher

  4. Department of Civil Engineering, High Institute of Technological Studies, Mrezgua University Campus, 8000, Nabeul, Tunisia

    Khaled Mohamed Khedher

  5. Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Tehran, Iran

    Mahro Dehbozorgi

  6. Department of Engineering, Wake Forest University, Winston-Salem, NC, 27101, USA

    Yasin Wahid Rabby

  7. HUTECH University, 475A, Dien Bien Phu, Ward 25, Binh Thanh District, Ho Chi Minh City, Vietnam

    Duong Tran Anh

  8. Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Duc Hiep Nguyen

  9. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Duc Hiep Nguyen

  10. Department of Planning, Industry and Environment, Wollongong, NSW, Australia

    Duc Hiep Nguyen

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  1. Sk Ajim Ali
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Ali, S.A., Parvin, F., Pham, Q.B. et al. An ensemble random forest tree with SVM, ANN, NBT, and LMT for landslide susceptibility mapping in the Rangit River watershed, India. Nat Hazards 113, 1601–1633 (2022). https://doi.org/10.1007/s11069-022-05360-5

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