Research Article | OPEN ACCESS
Spatial Prediction of Earthquake-Induced Secondary Landslide Disaster in Beichuan County Based on GIS
1, 2, 3Zhuowei Hu, 1, 2, 3Lai Wei, 1, 2, 3Dan Fang, 4Te Lai and 5Qing Wang
1College of Resources Environment and Tourism, Capital Normal University
2Kay Lab of Resources Environment and GIS
3Key Laboratory of Integrated Disaster Assessment and Risk
Governance of the Ministry of Civil Affairs, Beijing 100048, China
4Graduate School of Education
5Department of Geography, SUNY University at Buffalo, Buffalo 14221, USA
Research Journal of Applied Sciences, Engineering and Technology 2013 20:3828-3837
Received: January 17, 2013 | Accepted: February 22, 2013 | Published: 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.
Keywords:
Beichuan, earthquake, GIS, landslide, spatial prediction,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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