Landslide Susceptibility Zonation is an efficient technique decision-makers use for disaster mitigation in landslide-prone regions. This study proposes an alternate approach for LSZ mapping, aiming to mitigate the limitations of the subjective expert opinion-based methods presently employed by disaster management authorities in India. Consequently, a GIS-based ensemble of Frequency Ratio and Analytical Hierarchy Process is employed, which offers a more robust and objective evaluation of Landslide Susceptibility. A landslide inventory of 592 incidents is processed using the database maintained by the Geological Survey of India, the national nodal agency for landslide studies. Then, LSZ mapping is conducted for a selected region in the Indian Himalayas using the processed inventory and nine causative factors (Elevation, Slope, Aspect, Curvature, Terrain Ruggedness Index (TRI), Distance to drainage, Land Use/Land Cover (LULC), Geology, and Lithology) as input. The generated LSZ map is evaluated using separate subsets of the inventory, yielding accuracies of 74.13% and 75.08%, respectively, during the training and testing stages. The study's findings hold potential implications for more effective disaster mitigation strategies and early warning systems in landslide-prone regions.

Enhanced landslide susceptibility zonation using GIS-Based ensemble techniques

Claudia Cherubini
Ultimo
2025-01-01

Abstract

Landslide Susceptibility Zonation is an efficient technique decision-makers use for disaster mitigation in landslide-prone regions. This study proposes an alternate approach for LSZ mapping, aiming to mitigate the limitations of the subjective expert opinion-based methods presently employed by disaster management authorities in India. Consequently, a GIS-based ensemble of Frequency Ratio and Analytical Hierarchy Process is employed, which offers a more robust and objective evaluation of Landslide Susceptibility. A landslide inventory of 592 incidents is processed using the database maintained by the Geological Survey of India, the national nodal agency for landslide studies. Then, LSZ mapping is conducted for a selected region in the Indian Himalayas using the processed inventory and nine causative factors (Elevation, Slope, Aspect, Curvature, Terrain Ruggedness Index (TRI), Distance to drainage, Land Use/Land Cover (LULC), Geology, and Lithology) as input. The generated LSZ map is evaluated using separate subsets of the inventory, yielding accuracies of 74.13% and 75.08%, respectively, during the training and testing stages. The study's findings hold potential implications for more effective disaster mitigation strategies and early warning systems in landslide-prone regions.
2025
28-dic-2024
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3101738
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