Resumen
Early warning of safety risks downstream of small reservoirs is directly related to the safety of people?s lives and property and the economic and social development of the region. The lack of data and low collaboration in downstream safety management of small reservoirs makes the existing safety risk warning methods for small reservoirs no longer fully applicable. The data from flood control and drought relief departments, small reservoir operation and management departments, etc., are used comprehensively. A machine learning model suitable for a large number of samples, a small amount of data, and the condition of incomplete information is applied and innovated, and from the holistic perspective of ?upstream reservoir?downstream region?, the safety risk factors of the upstream reservoir are identified with the help of the Granger causality test. The risk losses of the disaster behavior are predicted with the three-dimensional ??~??
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two-equation model coupled with the VOF (Volume of fluid) method and the neural network model. The safety risk dynamics prediction, the prediction of the disaster-causing environment, and the prediction of the risk losses are integrated to construct the early warning method of the downstream safety risk of small reservoirs, and the simulation effect is verified with the example of the J Reservoir. The results show that the model can clarify the causal relationships and time lag dependencies between hydro-meteorological factors and the water level of small reservoirs, and calculate the inundation depth, inundation range, and flood velocity downstream of small reservoirs. The downstream safety warning model of small reservoirs constructed in this article can effectively integrate upstream and downstream information, further improve the timeliness and accuracy of warning, and provide a reference for downstream safety risk management of small reservoirs.