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Prediction of Shallow Landslide prone regions in Undulating Terrains

Ramachandra, TV and Aithal, Bharath H and Kumar, Uttam and Joshi, N (2013) Prediction of Shallow Landslide prone regions in Undulating Terrains. In: DISASTER ADVANCES, 6 (1). pp. 54-64.

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Genetic Algorithm for Rule-set Prediction (GARP) and Support Vector Machine (SVM) with free and open source software (FOSS) - Open Modeller were used to model the probable landslide occurrence points. Environmental layers such as aspect, digital elevation, flow accumulation, flow direction, slope, land cover, compound topographic index and precipitation have been used in modeling. Simulated output of these techniques is validated with the actual landslide occurrence points, which showed 92% (GARP) and 96% (SVM) accuracy considering precipitation in the wettest month and 91% and 94% accuracy considering precipitation in the wettest quarter of the year.

Item Type: Journal Article
Additional Information: Copyright for this article belongs to DISASTER ADVANCES, INDORE, INDIA
Keywords: Landslide;Genetic algorithm;Support Vector Machine
Department/Centre: Division of Biological Sciences > Centre for Ecological Sciences
Division of Interdisciplinary Sciences > Center for Infrastructure, Sustainable Transportation and Urban Planning (CiSTUP)
Division of Mechanical Sciences > Centre for Sustainable Technologies (formerly ASTRA)
Date Deposited: 07 Feb 2013 11:43
Last Modified: 29 Aug 2022 05:26
URI: https://eprints.iisc.ac.in/id/eprint/45715

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