基于BP神经网络的不同时间尺度泵站前池水位预测模型

Prediction model for forebay water level of pumping stations with different time scales based on BP neural networks

针对建筑物调控下的水位预测难题,建立基于BP神经网络的泵站前池水位预测模型,在不同时间尺度下分析时间序列、影响因子、预报因子对水位预测精度的影响。将构建模型应用于胶东调水工程东宋泵站,研究结果表明:在数据总量一定的情况下,训练期和预测期之比为7∶3时,预测结果较好;数据量越大,保持一定预报精度所需要的正相关的影响因子数量越多;短时间内,预测时间间隔与数据本身时间间隔相同时,预测效果更好。该构建模型能够满足明渠调水工程泵站前池的水位动态预测需求,实现泵站前池水位的2 h准确预测和4 h一般准确预测,同时可在其他类似明渠调水工程中推广应用。

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.