Prediction model for forebay water level of pumping stations with different time scales based on BP neural networks
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Abstract:
Considering the difficulty in water level prediction under building control,a water level prediction model for the forebay of a pumping station was built on the basis of back-propagation (BP) neural networks,and the influence of time series and impact factors on the accuracy of water level prediction was analyzed under different time scales.The constructed model was applied to the Dongsong Pumping Station of the Jiaodong Water Transfer Project.The research results revealed that:when the total amount of data was fixed,and the ratio of the training period to the prediction period was 7∶3,the prediction result was good;a larger amount of data was accompanied by a greater number of positively correlated impact factors required for certain prediction accuracy;in a short period of time,when the prediction time interval was the same as the time interval of the data itself,the prediction effect was better.The constructed model can meet the demand for dynamic prediction of the water level in the forebay of the open channel water transfer project and can achieve the 2 h accurate prediction of the forebay water level of the pumping station and the 4 h general accurate prediction.Additionally,it can be popularized and applied in other similar open channel water transfer projects.
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