Prediction of the spatial variability of rainfall- and human-induced landslides in the Bhagirathi Valley of the Indian NW Himalayas using machine learning techniques

Landslides are recurring and catastrophic natural hazards that cause tremendous loss in mountainous regions. Landslides in the Indian Himalayas are mainly due to rainfall, earthquakes, and anthropogenic activities such as deforestation, road widening, tunneling, and urbanization. Landslide susceptib...

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Published in:Environmental earth sciences Vol. 84; no. 21; p. 598
Main Authors: Gupta, Neha, Das, Josodhir, Kanungo, D. P.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2025
Springer Nature B.V
Subjects:
Algorithms
Anthropogenic factors
Artificial neural networks
Back propagation
Biogeosciences
Decision trees
Deforestation
Earth and Environmental Science
Earth Sciences
Earthquakes
Environmental Science and Engineering
Geochemistry
Geological hazards
Geology
Hazards
Human influences
Hydrology/Water Resources
Landslides
Landslides & mudslides
Learning algorithms
Machine learning
Mountain regions
Mountainous areas
Neural networks
Original Article
Precipitation
Predictions
Rainfall
Regression analysis
Risk assessment
Roads & highways
Seismic activity
Seismic response
Spatial variability
Spatial variations
Support vector machines
Terrestrial Pollution
Urbanization
Valleys
Widening
Brazil
Ecuador
Himalaya Mountains
Uttarakhand India
China
India
Ensemble models
Machine learning algorithms
Statistical models
Landslide susceptibility zonation mapping
Bhagirathi valley
ISSN:1866-6280, 1866-6299
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Summary:Landslides are recurring and catastrophic natural hazards that cause tremendous loss in mountainous regions. Landslides in the Indian Himalayas are mainly due to rainfall, earthquakes, and anthropogenic activities such as deforestation, road widening, tunneling, and urbanization. Landslide susceptibility mapping is a prerequisite for vulnerability and risk assessment. The present study aimed to evaluate the prediction of the landslide spatial variability for the Bhagirathi Valley in the Uttarakhand Himalayas, India, using machine learning and statistical algorithms, namely, Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbour (KNN), Artificial Neural Network (ANN), Frequency Ratio (FR) and Certainty Factor (CF). A geospatial database with 3098 landslide polygons that are either rainfall-triggered or due to road widening, as well as thematic layers pertaining to 12 landslide causative factors, were prepared for this study. The earthquake-induced landslides, referring to the 20th October 1991 Uttarkashi earthquake with a moment magnitude of 6.8 and a maximum Mercalli intensity of IX, were excluded from the database. Hence, the present spatial variability prediction has been attempted using the rainfall- and anthropogenic-induced landslides database. In terms of model robustness, the area under the curve (AUC) reached its highest value of 0.91 for XGBoost. The AUC values for the other models were as follows: RF at 0.90, KNN at 0.89, ANN at 0.89, CF at 0.89, and FR at 0.88. The study demonstrated that ensemble tree-based models are more effective in predicting landslide spatial variability than other techniques. The results emphasized that proper model selection and balanced complexity can significantly enhance performance. The landslide susceptibility maps thus produced will be helpful for decision-makers and planners in assessing the vulnerability and risk of the Bhagirathi Valley. The Bhagirathi Valley is considered as one of the northwestern Himalayan valleys most affected by landslide hazards.
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ISSN:1866-6280
1866-6299
DOI:10.1007/s12665-025-12568-8