Abstract

Flash flooding is a type of global devastating hydrometeorological disaster that seriously threatens people’s property and physical safety, as well as the normal operation of water conservancy facilities, such as reservoirs, so an accurate assessment of reservoir risk for certain areas is necessary. Therefore, the purpose of this study was to propose a novel methodological approach for reservoir risk modelling based on the feature selection method (FSM) and tree-based ensemble methods (Bagging and Random Forest [RF]). The results showed that: (1) the J48-GA based ensemble models achieved higher learning and predictive capabilities compared to conventional ensemble models without the FSM. (2) For the classification accuracy, the J48-GA-RF (96.4%) outperformed RF (96.0%), J48-GA-Bagging (93.9%) and Bagging (93.5%). And the J48-GA-RF achieved the highest prediction AUC value (0.995), an almost perfect Kappa indexes value (0.926) and the best practicality value (30.88%). (3) In particular, the results indicated that all of the models showed high performance, both in training and in the validation of a dataset. Additionally, this study could provide a reference for disaster managers, hydraulic engineers and policy makers to implement location-specific flash flood risk reduction strategies.

摘要

山洪暴发是一种全球性破坏性水文气象灾害，严重威胁人们的财产和人身安全，以及水库等水利设施的正常运行，因此需要对特定区域的水库风险进行准确评估。因此，本研究的目的是提出一种基于特征选择方法 (FSM) 和基于树的集成方法 (Bagging 和随机森林 [RF]) 的储层风险建模新方法。结果表明：（1）与没有 FSM 的传统集成模型相比，基于 J48-GA 的集成模型实现了更高的学习和预测能力。(2) 对于分类准确率，J48-GA-RF (96.4%) 优于 RF (96.0%)、J48-GA-Bagging (93.9%) 和 Bagging (93.5%)。其中J48-GA-RF的预测AUC值最高（0.995），Kappa指数接近完美（0.926），实用性最好（30.88%）。(3) 特别是，结果表明所有模型在训练和数据集验证方面都表现出高性能。此外，本研究可为灾害管理者、水利工程师和政策制定者实施针对特定地点的山洪灾害风险降低策略提供参考。