准确的易发性评价结果能够对滑坡带来的危险进行精准防控.样本优化是滑坡易发性评价的重要方法,可有效解决不平衡样本产生的决策边界偏移问题,提升滑坡易发性评价精度.以中国重庆市万州区东南区域为例,选取地层、土地利用、高程等10个影响因子构建滑坡易发性评价指标体系,应用频率比方法定量分析滑坡与指标之间的关系,在此基础上分别利用深度神经网络模型(deep neural networks,DNN)、过采样-深度神经网络模型(synthetic minority oversam-pling technique-DNN,SMOTE-DNN)、混合采样-深度神经网络耦合模型(one-class support vector machine-SMOTE-DNN,OS-DNN)、混合采样-深度神经网络-K均值聚类耦合模型(OS-DNN-K-means)进行滑坡易发性评价.结果表明,距道路距离、土地利用、地层是研究区滑坡发育的主要控制因子.精度评价结果发现OS-DNN-K-means(95.61％)和OS-DNN(91.16％)相较于模型SMOTE-DNN(87.97％)和DNN(81.40％)更能有效提高滑坡预测精度.通过混合采样和半监督分类进行样本优化能够有效解决研究区样本不平衡问题,为滑坡灾害空间预测提供新技术支撑.
Evaluation of Landslide Susceptibility Based on Sample Optimization Strategy
Objectives:Accurate susceptibility evaluation results can accurately prevent and control the dan-gers caused by landslides.Sample optimization is an important method for landslide susceptibility evalua-tion,which can effectively solve the problem of decision boundary offset generated by unbalanced samples and improve the accuracy of landslide susceptibility evaluation.Methods:Taking the southeast area of Wan-zhou District of Chongqing,China as an example,ten influencing factors such as strata,land use and eleva-tion were selected to construct a landslide susceptibility evaluation index system,and the relationship be-tween landslide and the indices was quantitatively analyzed by frequency ratio method,and on this basis,deep neural network model(DNN),synthetic minority oversampling technique-DNN model(SMOTE-DNN),one-class support vector machine-DNN coupling model(OS-DNN),and OS-DNN-K-means clustering coupling model(OS-DNN-K-means)were used to evaluate landslide susceptibility.Results:The results show that the distance from the road,land use and strata are the main control factors for land-slide development in the study area.The accuracy evaluation results show that OS-DNN-K-means(95.61％)and OS-DNN(91.16％)could improve the landslide prediction accuracy more effectively com-pared with SMOTE-DNN(87.97％)and DNN(81.40％).Conclusions:Sample optimization through mixed sampling and semi-supervised classification can effectively solve the problem of sample imbalance in the study area,and provide new technical support for spatial prediction of landslide disasters.
万方数据
维普
