Prediction model for water level regulation in water diversion project based on BP neural network
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Abstract:
In a water diversion project, if the water level before the pump is too low, the security of the pumping station will be endangered; if the water level is too high, the safety of the surrounding area will be endangered. So, it is particularly important to study the change of water level in the reservoir. The paper, based on the Nansi Lake section of the eastern route of the South to North Water Transfer Project in Shandong Province, explores the water level variation pattern before the pump under different conditions of initial water level, inflow and outflow, and opening time differ ence between pumping stations. Firstly, we used the coupled model to numerically simulate different diversion plans. Then we selected 23 water diversion plans and their beforepump water level obtained from numerical simulation as samples to train the BP neural network. Thus, we established a prediction model for water level regulation and had it veri fied. Finally we predicted the water level before the pump in different diversion plans using the model. The results showed that the BP neural network model has a strong predictive power. Its results were basically consistent with the results of the coupled model, with the relative error of water depth less than 9. 15% . Meanw hile the modelcs calculation efficiency improved by 96. 67% .
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