Comparison of different sampling strategies for debris flow susceptibility mapping: A case study using the centroids of the scarp area, flowing area and accumulation area of debris flow watersheds  

作　　者：GAO Rui-yuan WANG Chang-ming LIANG Zhu 

机构地区：[1]College of Construction Engineering,Jilin University,Changchun 130012,China

出　　处：《Journal of Mountain Science》2021年第6期1476-1488,共13页山地科学学报（英文）

基　　金：This work was supported by National Natural Science Foundation of China(Grant no.41972267 and no.41572257);Graduate Innovation Fund of Jilin University(Grant no.101832020CX232)。

摘　　要：The quality of debris flow susceptibility mapping varies with sampling strategies. This paper aims at comparing three sampling strategies and determining the optimal one to sample the debris flow watersheds. The three sampling strategies studied were the centroid of the scarp area(COSA), the centroid of the flowing area(COFA), and the centroid of the accumulation area(COAA) of debris flow watersheds. An inventory consisting of 150 debris flow watersheds and 12 conditioning factors were prepared for research. Firstly, the information gain ratio(IGR) method was used to analyze the predictive ability of the conditioning factors. Subsequently, 12 conditioning factors were involved in the modeling of artificial neural network(ANN), random forest(RF) and support vector machine(SVM). Then, the receiver operating characteristic curves(ROC) and the area under curves(AUC) were used to evaluate the model performance. Finally, a scoring system was used to score the quality of the debris flow susceptibility maps. Samples obtained from the accumulation area have the strongest predictive ability and can make the models achieve the best performance. The AUC values corresponding to the best model performance on the validation dataset were 0.861, 0.804 and 0.856 for SVM, ANN and RF respectively. The sampling strategy of the centroid of the scarp area is optimal with the highest quality of debris flow susceptibility maps having scores of 373470, 393241 and 362485 for SVM, ANN and RF respectively.

关 键 词：Debris flow Artificial neural network Support vector machine Random forest SUSCEPTIBILITY Sampling strategy 

分 类 号：P642.23[天文地球—工程地质学]