Classification Assessment of Debris Flow Susceptibility in Bailong River Basin Based on PU-Bagging Negative Sampling
Wan Qinjiang
Zheng Hongchao
Wang Honglei
Wu Bin
Shi Zhenming
Li Yuanwei
Abstract：To improve the reliability of the debris flow-prone zones in the Bailong River Basin,a PU-Bagging negative sampling model based on the random forest as the base learner is established.Evaluation factors such as elevation and precipitation were selected,and logistic regression,random forest,support vector machine and XGBoost algorithms were used to construct an evaluation model for the susceptibility of debris flows in the Bailong River Basin.Based on the evaluation indicators derived from the confusion matrix,the ROC curve and five classification methods,the performances of the four models were compared and analyzed,and the contribution degree of the evaluation factors to the model was analyzed by using SHAP.The results show follows.(1)The disaster identification accuracy of the support vector machine model combined with the geometric interval classification method has increased by 24%.(2)The random forest model can identify more potential debris flow samples,while the XGBoost model can reduce the misjudgment of non-disaster samples.(3)The sensitivity of SHAP values to elevation changes indirectly reflects the importance of height differences for the development of debris flows.This research can provide data support for the planning of the new urbanization construction and debris flow prevention and control project in the Bailong River Basin.
Keywords：Bailong River Basinnegative samplingsusceptibilitydebris flowclassification methodengineering geology
Publication Date：2025-10-30
Online Publishing Date：2025-11-11(First online date of this platform, not the publication date of the document)
Pages：15( 4044-4058 )