Prediction of Shallow Landslide prone regions in Undulating Terrains


Autoria(s): Ramachandra, T; Aithal, Bharath H; Kumar, Uttam; Joshi, N
Data(s)

2013

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