摘要
以地震滑坡作为研究对象,选取"4·20"芦山地震中芦山县为研究区,结合多源数据,在相关分析后选取10个评价因子,分别是地面高程、坡度、坡向、斜坡形态、地层、斜坡结构、断层平均距离、水系平均距离、植被指数和地震峰值加速度,在数字高程模型基础上采用集水区重叠法划分斜坡单元,再对各评价因子重采样,进而利用基于遗传算法的神经网络算法构建地震滑坡危险性评价模型,完成地震滑坡危险性区划。将基于斜坡单元的危险性区划结果和基于格网单元的区划结果进行比较,结果显示滑坡正确率分别为96.6%和92.6%,斜坡单元的正确率较高;同时通过多组数据的受试者工作特征曲线分析本评价模型的不确定性,每组曲线位置及曲线下面积大小相当。
Earthquake-induced landslide could cause serious damage,which could be even worse than the quake itself especial in a mountain area.We select Lushan as the research area where Lushan earthquake occurred on April 20,2013.This earthquake caused massive landslides that resulted in a tragic loss of life and economy.Combining multi-source data,we select 10 evaluation factors after Pearson correlation analysis,including elevation,slope,aspect,shape,curvature classification,vegetation index,distance from drainages,slope structure,lithology,and distance from faults.Based on the digital elevation model,we use watershed overlay method to plot slope units,and resample theevaluation factors.After multi-group test,we get the appropriate parameter value of the neural network optimized by Genetic Algorithm model.The risk zoning of earthquake induced landslides is then calculated by using neural network optimized by Genetic Algorithm.The same risk zoning model is used to slope units and grid units.The accuracy rate of slope units is 96.6%;while the accuracy rate of grid units is 92.6%.In comparison with the grid units,the slope units are more accurate.Further,we analyze the uncertainty in this evaluation model by using multi-data receiver operating characteristic curve（ROC）.Each curve has the equal position;and the areas under the line are almost the same.The result indicates that the proposed method has the advantages of high accuracy,stable performance,and small amount of data to process.It can be used for early warning and assessment of earthquake.
出处
《吉林大学学报（地球科学版）》
EI
CAS
CSCD
北大核心
2015年第5期1470-1478,共9页
Journal of Jilin University：Earth Science Edition
基金
国家"863"计划项目(2012AA121303)
关键词
地震滑坡
危险性区划
评价因子
斜坡单元
四川芦山
earthquake-induced landslide
risk zoning
evaluation factors
slope units
Lushan
Sichuan
