Reservoir risk modelling using a hybrid approach based on the feature selection technique and ensemble methods

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.

Keywords:

• Flash flood
• reservoir risk
• J48 Decision Tree
• genetic algorithm
• Bagging
• random forest
• China

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported by the Strategic Priority Research Program of Chinese Academy of Sciences [grant no. XDA20030302], The Science and Technology Project of Xizang Autonomous Region [grant no. XZ201901-GA-07], Southwest Petroleum University of Science and Technology Innovation Team Projects [grant no. 2017CXTD09] and National lash Flood Investigation and Evaluation Project [grant no. SHZH-IWHR-57].