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Prediction of spatial landslide susceptibility applying the novel ensembles of CNN, GLM and random forest in the Indian Himalayan region

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Abstract

This research aims to generate a landslide susceptibility map (LSM) for the Bhagirathi river basin located in the Tehri Garhwal district of Uttarakhand state in India. For this study, we incorporated and utilized machine learning novel ensemble models, namely: Generalize Linear Model (GLM), Random Forest (RF), Convolutional Neural Network (CNN), GLM-RF, CNN-RF, CNN-GLM and CNN-GLM-RF. The above-specified ensemble models were incorporated for the preparation of LSM. A total of 171 landslide locations were chosen and studied for the preparation of landslide inventory. From the landslide inventory, 70% of landslides were utilized for the training purpose, whereas the rest, 30%, were used for the validation purpose. In the ongoing research study, a total of 17 landslide conditioning factors (LCFs) were utilized, and the analysis for multi-collinearity was performed by tolerance (TOL) as well as variance inflation factor (VIF) methods. These LSM were then validated through receiver operating characteristic curve (ROC), mean absolute error (MAE), root mean square error (RMSE) and Chi-Square methods. Finally, the outcomes of the analysis, as well as validation models, show that the CNN model proves to be the most effective for predicting landslide susceptibility in the study area. The study found that the methods incorporated for the area under consideration can be applied to the regions having similar geo-environmental factors globally.

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Authors and Affiliations

  1. Department of Geography, University of Gour Banga, Malda, West Bengal, 732103, India

    Sunil Saha, Anik Saha & Kanu Mandal

  2. Department of Geography, Nistarini Women’s College, Purulia, West Bengal, India

    Tusar Kanti Hembram

  3. Department of Civil Engineering, Delhi Technological University, Delhi, 110 042, India

    Raju Sarkar & Dhruv Bhardwaj

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  1. Sunil Saha
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Saha, S., Saha, A., Hembram, T.K. et al. Prediction of spatial landslide susceptibility applying the novel ensembles of CNN, GLM and random forest in the Indian Himalayan region. Stoch Environ Res Risk Assess 36, 3597–3616 (2022). https://doi.org/10.1007/s00477-022-02212-3

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