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01-06-2024 | Original Paper

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

Authors: Ramandeep Kaur, Vikram Gupta, B. S. Chaudhary

Published in: Bulletin of Engineering Geology and the Environment | Issue 6/2024

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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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Metadata
Title
Landslide susceptibility mapping and sensitivity analysis using various machine learning models: a case study of Beas valley, Indian Himalaya
Authors
Ramandeep Kaur
Vikram Gupta
B. S. Chaudhary
Publication date
01-06-2024
Publisher
Springer Berlin Heidelberg
Published in
Bulletin of Engineering Geology and the Environment / Issue 6/2024
Print ISSN: 1435-9529
Electronic ISSN: 1435-9537
DOI
https://doi.org/10.1007/s10064-024-03712-y

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