A hybrid deep learning model based on feature capture of water level influencing factors and prediction error correction for water level prediction of cascade hydropower stations under multiple time scales

Abstract

The operating conditions of large cascade hydropower stations are complex. Improving the water level prediction accuracy of large cascade hydropower stations is significant for flood control, shipping, irrigation, etc. A new hybrid model based on a refined deep residual shrinkage network and an optimized gated recurrent unit – long term memory network (GRU-LSTM) model is proposed for water level prediction at different time scales. First, to make the input data in the form of a feature map so the model can capture the weight characteristics of each influencing factor when the water level changes, the water level, flow rate, and hydroelectric power plant output data are constructed as high-dimensional feature inputs. To reduce the prediction error caused by downstream tributary backwater jacking, downstream tributary flow is added to the input data. At the same time, a semisoft threshold function with an adjustable function is inserted into the model to improve feature recognition accuracy, reduce deviation, and eliminate the noise of the original hydrological data. Second, an error weight correction function is used to adjust the error between the predicted water level and the observed water level so that the model can automatically adjust the weights of each influencing factor according to the evaluation results of water level prediction. In this process, the parameters of LSTM are optimized using the Archimedes optimization algorithm, and the output results of the LSTM and GRU networks are weighted to obtain a more accurate water level prediction. The proposed model achieves good accuracy and efficiency in downstream water level prediction of the Xiangjiaba Hydropower Station, outperforms existing hydrodynamic and artificial intelligence methods, and has robust scalability in flood forecasting and urban rainwater forecasting.