What question did this study set out to answer?

This study aims to compare machine learning and deep learning models for spatial landslide modeling in the Tehri Garhwal District.

June 26, 2026Open Access

Comparative evaluation of ML and DL approaches for spatial landslide modeling in Tehri Garhwal, India

SSSunil SahaUniversity of Gour Banga ASAnik SahaUniversity of Gour Banga SJSubhendu JanaBaylor University

Key Points

This study aims to compare machine learning and deep learning models for spatial landslide modeling in the Tehri Garhwal District.
Models were trained and tested using a 70:30 split from 1,600 landslide locations and 17 conditioning factors.
Model performance was assessed using area under the curve, mean absolute error, and root mean square error.
An integrated evaluation approach applied recent landslide inventory and conditioning datasets.
Deep learning models achieved the highest accuracy of 90.31% using recent datasets.
Machine learning models were less accurate, demonstrating lower effectiveness in predicting landslide susceptibility.
Findings provide insights for developing reliable landslide susceptibility maps for planning and risk mitigation.

Abstract

Abstract Landslides are significant natural hazard in the Tehri Garhwal District in Uttarakhand, India, posing risks to human life, infrastructure, and environment. As the district ranks second among 147 districts based on the landslide index, accurate spatial modelling of landslide susceptibility is crucial for effective risk management. This study presents a comprehensive comparative assessment of machine learning (ML) and deep learning (DL) models for spatial landslide modelling. The study further examines the varying correlations between conditioning factors and the occurrence of landslides. The models were trained and tested using a 70:30 split, with 17 landslide conditioning factors and 1,600 landslide locations. Model performance was assessed using the Area Under the Receiver Operating Characteristic Curve, mean absolute error, and root mean square error. The novelty of this study lies in the integrated evaluation of conventional machine-learning and deep-learning models under a unified framework using the most recent and updated landslide inventory and conditioning datasets for the study area. The accuracy of the deep learning neural network models was the highest (90.31%), using the most recent datasets. The outcomes of this study provide valuable insights for researchers and practitioners in the development of reliable landslide susceptibility maps for evidence-based planning and risk mitigation in mountainous regions.

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