Prediction of Shallow Landslide prone regions in Undulating Terrains
Data(s) |
2013
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Resumo |
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. |
Formato |
application/pdf |
Identificador |
http://eprints.iisc.ernet.in/45715/1/dis_adv_6-1_54_2012.pdf Ramachandra, T 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. |
Publicador |
DISASTER ADVANCES |
Relação |
http://www.disasterjournal.net/BackIssue.php http://eprints.iisc.ernet.in/45715/ |
Palavras-Chave | #Centre for Ecological Sciences #Center for infrastructure, Sustainable Transportation and Urban Planning (CiSTUP) #Centre for Sustainable Technologies (formerly ASTRA) |
Tipo |
Journal Article PeerReviewed |