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Prediction of Shallow Landslide prone regions in Undulating Terrains
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T.V. Ramachandra1,2,3,*               Bharath H. Aithal1,2               Uttam Kumar 1               Joshi N V1
1 Energy and Wetlands Research Group, Centre for Ecological Sciences [CES], 2 Centre for Sustainable Technologies (astra),
3 Centre for infrastructure, Sustainable Transportation and Urban Planning [CiSTUP], Indian Institute of Science, Bangalore – 560012, India.
*Corresponding author: cestvr@ces.iisc.ernet.in

Abstract

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.

Keywords: Landslide, Genetic algorithm, Support Vector Machine.

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Citation : Ramachandra. T.V., Bharath H. Aithal, Uttam Kumar and Joshi N. V., 2013. Prediction of Shallow Landslide prone regions in Undulating Terrains., Disaster Advances, Vol. 6(1) January 2013, pp. 53-63.
* Corresponding Author :
Dr. T.V. Ramachandra
Energy & Wetlands Research Group, Centre for Ecological Sciences, Indian Institute of Science, Bangalore – 560 012, India.
Tel : +91-80-2293 3099/2293 3503-extn 107,      Fax : 91-80-23601428 / 23600085 / 23600683 [CES-TVR]
E-mail : cestvr@ces.iisc.ernet.in, energy@ces.iisc.ernet.in,     Web : http://wgbis.ces.iisc.ernet.in/energy, http://ces.iisc.ernet.in/grass
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