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Landslide susceptibility mapping and sensitivity analysis using various machine learning models: a case study of Beas valley, Indian Himalaya

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Abstract

Landslide is one of the most destructive hazards in the Upper Beas valley of the Himalayan region of India. Landslide susceptibility mapping is an important and preliminary task in order to prospect the spatial variability of landslide prone zones in the area. As the use of machine learning algorithms has increased the success rate in susceptibility studies, the performance of the four machine learning models, namely Naïve Bayes (NB), K-Nearest Neighbor (KNN), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were initially tested for landslide susceptibility mapping in the area. Landslide inventory containing both landslide and non-landslide data and thirteen landslide conditioning factors were considered to train the models. The models were optimized using hyperparameter optimization and input factors selection based on variable importance. Among the four models, Extreme Gradient Boosting (XGBoost), an advanced ensemble-based machine learning algorithm, demonstrated superior performance (AUC = ~ 0.91) followed by RF, NB and KNN with AUC values of ~ 0.88, ~ 0.87, and ~ 0.82. Therefore, XGboost model was selected for detailed study, including sensitivity analysis. The results depict that 44% of the total area falls under high and very high susceptible zones. Southward facing slopes having inclination between 31˚-50˚ located at an elevation of 2001–3000 m in the vicinity of road and drainage network contain most of the landslide susceptible zones. Sensitivity analysis has provided an in-depth understanding of the factors’ relation with the model as the XGBoost model is most sensitive to factors such as slope inclination, distance to thrust and road, elevation, TWI and slope aspect.

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Data availability

All data included in this study are available upon request by contact with the corresponding author.

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Acknowledgements

The authors thank the Director, Wadia Institute of Himalayan Geology, Dehradun for his constant encouragement and providing necessary facilities to carry out the work. RK acknowledges the financial assistance in the form of fellowship from Council of Scientific and Industrial Research (CSIR), New Delhi. Dr. Bikash Ram and Ambar Solanki are also thanked for their precious assistance in writing the article. The article bears the Wadia Institute contribution number WIHG/0248.

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  1. Wadia Institute of Himalayan Geology, Dehradun, Uttarakhand, India

    Ramandeep Kaur & Vikram Gupta

  2. Department of Geophysics, Kurukshetra University, Kurukshetra, Haryana, India

    Ramandeep Kaur & B. S. Chaudhary

  3. Department of Geology, Sikkim University, Gangtok, Sikkim, India

    Vikram Gupta

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Kaur, R., Gupta, V. & Chaudhary, B.S. Landslide susceptibility mapping and sensitivity analysis using various machine learning models: a case study of Beas valley, Indian Himalaya. Bull Eng Geol Environ 83, 228 (2024). https://doi.org/10.1007/s10064-024-03712-y

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