Data-Driven Parameter Prediction of Water Pumping Station

Abstract

In the construction process of an intelligent pumping station, the parameter calibration of the pumping station unit is very important. In actual engineering, the working parameters of the pumping station are affected by complex working conditions and natural factors, so that it is difficult to establish a traditional physical model for the pumping station. This paper uses a data-driven method to apply the hybrid model of the convolutional neural network (CNN) and long-term short-term memory network (LSTM) to water level prediction in pumping stations and adds self-attention mechanism feature selection and a bagging optimization algorithm. Then, after an error analysis of the hybrid model, a performance comparison experiment with the separate model was conducted. The historical data of the pumping station project provided by the Tuancheng Lake Management Office of Beijing South-to-North Water Diversion Project was used to train and verify the proposed pumping station water level prediction model. The results show that the CNN–LSTM model based on the self-attention mechanism has higher accuracy than the separate CNN model and LSTM model, with a correlation coefficient (R2) of 0.72 and a mean absolute error (MAE) of 19.14. The model can effectively solve the problem of water level prediction in the front and rear pools under complex pumping station conditions.