基于GIS的北川县地震次生滑坡灾害空间预测

自然灾害的预测预报被认为是主动减灾防灾研究中较为经济有效的方式,其中,滑坡空间预测是滑坡灾害研究的基础工作。以汶川地震重灾区北川县为研究区,选取坡度、高程、岩石类型、地震烈度、水系、道路等6个重要滑坡影响因素作为评价因子,全面分析了地震滑坡分布与各影响因子之间的统计相关性,分别采用多元回归模型与神经网络模型计算滑坡灾害敏感性指数,并进行分级和制图。结果表明,极高和高敏感区主要分布在曲山、陈家坝等乡镇,主要沿着龙门山断裂带周边地区的河流和道路呈带状分布。其中,回归模型的预测精度为73.7%,神经网络模型的预测精度为81.28%,在本区域内,神经网络模型在滑坡灾害空间预测方面更具优势。

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

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,litho logy,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 model 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.