Research on dam break risk assessment and prevention based on deep learning and CE/SE methods
ZHANG Weixiong
TANG Xinwu
ZHANG Xun
MO Jianan
Abstract：To effectively enhance the capability of dam break prevention and control,this article proposes a dam break risk assessment and prevention model that combines deep learning with conservative and dynamic element methods(CE/SE).It provides a detailed introduction to the theoretical framework,method implementation,and practical engineering applications of the model,and validates its effectiveness and reliability through experiments.This model utilizes deep learning algorithms to extract dam break risk features from historical data,and combines the CE/SE method for high-precision numerical simulation of dam break flood waves,achieving accurate prediction and effective prevention and control of dam break risk.The results indicate that this model can significantly improve the accuracy of dam break risk assessment,providing a scientific basis for dam break prevention and control.
Keywords：deep learningdam break riskprevention and control
Publication Date：2025-09-25
Pages：4( 128-131 )