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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月23日

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  • Sunil Saha,
  • Anik Saha,
  • Subhendu Jana,
  • Priyanka Gogoi,
  • Somnath Rudra,
  • Biswajeet Pradhan

摘要

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.