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    Abstract
2013 (Vol. 6, Issue: 20)
Article Information:

Spatial Prediction of Earthquake-Induced Secondary Landslide Disaster in Beichuan County Based on GIS

Zhuowei Hu, Lai Wei, Dan Fang, Te Lai and Qing Wang
Corresponding Author:  Lai Wei 

Key words:  Beichuan, earthquake, GIS, landslide, spatial prediction, ,
Vol. 6 , (20): 3828-3837
Submitted Accepted Published
January 17, 2013 February 22, 2013 November 10, 2013
Abstract:

In earthquake-stricken area, with the occurrence of aftershocks, heavy rainfall and human activity, the earthquake-induced secondary landslide disaster will threaten people’s life and property in a very long period. So, it makes secondary landslide became a research hotspots that draw much attention. The forecasting of natural disaster is considered as a most effective way to prevention or mitigation disaster and the spatial prediction is the base work of landslide disaster research. The aim of this study is to analyze the landslide prediction, taking the case of Beichuan County. Six factors affecting landslide occurrence have been taken into account, including elevation, slope, lithology, seismic intensity, distance to roads and rivers. The correlations of landslide distribution with these factors is calculated, the multiple regression and neural network model are applied to landslide spatial prediction and mapping. The model calculates result is ultimately categorized into four classes. It shows that the high and very high susceptibility areas most distribute in Qushan, Chenjiaba towns, etc., along the rivers and the roads around the area of Longmenshan fault. The precision accuracy using multiple regression models is about 73.7% and the neural network model can be up to 81.28%. It can be concluded that in this study area, the neural network model appears to be more accurate in landslide spatial prediction.
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  Cite this Reference:
Zhuowei Hu, Lai Wei, Dan Fang, Te Lai and Qing Wang, 2013. Spatial Prediction of Earthquake-Induced Secondary Landslide Disaster in Beichuan County Based on GIS.  Research Journal of Applied Sciences, Engineering and Technology, 6(20): 3828-3837.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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