基于神经网络模型的斜坡地质灾害易发性评价:以吉林永吉为例

吉林省永吉县存在大量的斜坡地质灾害,为了给永吉县斜坡地质灾害的防治和预警提供高效直观的分析模型,将吉林省永吉县作为研究区,选取高程、坡度、坡向、剖面曲率、平面曲率、距断层距离、岩性、距河流距离、年均降雨量、地形湿度指数和植被覆盖指数等11个评价因子,利用神经网络模型进行区域斜坡地质灾害易发性分析,再选用频率比、支持向量机模型进行对比。利用ROC曲线对模型的准确性进行验证分析,得出神经网络、频率比和支持向量机模型的成功率分别是91.3%、89.3%、90.2%,预测率分别是87.3%、84.3%、85.6%。结果表明:神经网络模型的精度最高,更适用于永吉县斜坡地质灾害的易发性评价。

基于Stacking集成学习的泥石流易发性评价:以四川省雅江县为例

为给四川省雅江县提供直观准确的泥石流易发性图,将四川省雅江县作为研究区,选用高程、坡度、坡向、地形起伏度、平面曲率、剖面曲率、年平均降雨、到河流的距离、到道路的距离、地形湿度指数、归一化差分植被指数及土壤类型12个评价因子,利用Stacking集成学习框架,结合支持向量机、神经网络和随机森林,建立了一种多模型融合的泥石流预测模型。通过ROC曲线验证了模型的准确性,得出Stacking融合模型、随机森林、神经网络和支持向量机模型的成功率分别是98.1%、96.1%、94.5%、93.4%,预测率分别是95.5%、91.6%、90.6%、89.7%。结果表明:Stacking融合模型精度最高,最适合用于雅江县泥石流易发性评价。

Development of a region-partitioning method for debris flow susceptibility mapping

Debris flow susceptibility mapping(DFSM)has been reported in many studies,however,the irrational use of the same conditioning factor system for DFSM in regional-scale has not been thoroughly resolved.In this paper,a region-partitioning method that is based on the topographic characteristics of watershed units was developed with the objective of establishing multiple conditioning factor systems for regional-scale DFSM.First,watershed units were selected as the mapping units and created throughout the entire research area.Four topographical factors,namely,elevation,slope,aspect and relative height difference,were selected as the basis for clustering watershed units.The k-means clustering analysis was used to cluster the watershed units according to their topographic characteristics to partition the study area into several parts.Then,the information gain ratio method was used to filter out superfluous factors to establish conditioning factor systems in each region for the subsequent debris flow susceptibility modeling.Last,a debris flow susceptibility map of the whole study area was acquired by merging the maps from all parts.DFSM of Yongji County in Jilin Province,China was selected as a case study,and the analytical hierarchy process method was used to conduct a comparative analysis to evaluate the performance of the region-partitioning method.The area under curve(AUC)values showed that the partitioning of the study area into two parts improved the prediction rate from 0.812 to 0.916.The results demonstrate that the region-partitioning method on the basis of topographic characteristics of watershed units can realize more reasonable regional-scale DFSM.Hence,the developed region-partitioning method can be used as a guide for regional-scale DFSM to mitigate the imminent debris flow risk.