量纲统一在滑坡易发性评价中的影响分析

Analysis of the influence of dimensional unity in landslide susceptibility assessment

以往的区域性滑坡易发性评价研究多以对比不同评价模型结果和改进模型为主,而忽视了所选致灾因子的信息保留以及因子量纲如何统一的问题。为探究致灾因子的相关性和量纲对易发性评价结果的影响,以甘肃省靖远县北部地区作为研究区,选取高程、坡度、坡向和地形起伏度等12个因子,利用主成分分析提取的新因子参与易发性评价,并采用数据标准化、滑坡密度和信息量值替代法统一致灾因子的量纲,最后基于GIS平台绘制研究区滑坡易发性分区图。通过ROC曲线评估各模型的易发性评价结果精度。结果表明:在信息量模型、逻辑回归模型和感知机模型中,经主成分分析处理的因子得到的模型评价结果精度更高,采用信息量值替代法统一因子的量纲能够进一步提升逻辑回归和感知机模型的评价结果精度;同时,3种评价模型中感知机模型的结果精度最高(AUC=0.9367),优于信息量模型(AUC=0.9173)和逻辑回归模型(AUC=0.9272),是该研究区滑坡易发性评价的理想模型,应优先考虑。研究结果可为类似地区的防灾减灾工作提供基础数据和理论参考。

Previous studies on the susceptibility assessment of regional landslides mainly focused on comparing and improving the results of different evaluation models,while neglected the preservation of information on selected disaster-causing factors and the issue of how to unify factor dimensions.To explore the correlation and dimensionality of disaster causing-factors and their impact on susceptibility assessment,this study selected 12 factors such as elevation,slope,aspect,and terrain undulation,and used new factors extracted from principal component analysis in susceptibility assessment in the northern Jingyuan County.Data standardization,landslide density,and information quantity substitution methods were used to unify the dimensionality of disaster-causing factors.The landslide susceptibility zoning map was drawn based on the GIS platform in the study area.The accuracy of the susceptibility assessment of each mode was evaluated by the receiver operating characteristic curve.The results show that among the information model,the logistic regression model,and the perceptron model,the accuracy of the model evaluation obtained by the factors processed by principal component analysis is the highest.Using the information value substitution method to unify the dimensions of factors can further improve the accuracy of the evaluation of the logistic regression model and the perceptron model.The perceptron model has the highest accuracy(AUC=0.9367),which is superior to the information model(AUC=0.9173)and the logistic regression model(AUC=0.9272).This is an ideal model for the landslide susceptibility assessment in the study area and should be given priority.The results can provide the basic theoretical information for disaster prevention and mitigation in similar areas.