论文--中国科学院地理科学与资源研究所

论文

论文题目：	Reservoir risk modelling using a hybrid approach based on the feature selection technique and ensemble methods
第一作者：	Xiong Junnan, Pang Quan, Cheng Weiming, Wang Nan etc.
联系作者：
发表年度：	2022
摘　　要：	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.
英文摘要：
刊物名称：	GEOCARTO INTERNATIONAL
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论文类别：	SCI