A novel deep learning based spatial ensemble approach and segment anything model for landslide risk assessment in Chamoli district of Garhwal Himalayas.

ABSTRACT: This study aims at introducing novel "Spatial Ensemble" approach for landslide risk assessment (LRA). For this purpose, the present work focuses on three different algorithms that includes a state-of-the-art deep learning DenseNet Neural Network, and 2 popular benchmark algorithms of MLPNN and XGBoost. The spatial ensemble approach in LRA focuses on generating two distinct types of "Landslide Impedance Composite Maps", where the first one predicts the broadest possible danger zones covering the maximum extent with potential landslide occurrences across the study area. The second map targets the places in all probability to encounter landslides, by demarcating specific locations with the greatest risk of landslide occurrences to prioritize accuracy and precision, making sure that areas with

SUBMITTER: Mandal S 

PROVIDER: S-EPMC12698865 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature