Real-time water level prediction of cascaded channels based on multilayer perception and recurrent neural network

Abstract

Water level prediction is crucial to water diversion through cascaded channels, and the prediction accuracies are still unsatisfying due to the difficulties and challenges caused by complex interactions and relations among cascaded channels. We adopt two kinds of neural networks to build our water level prediction models for cascaded channels 2/4/6 h ahead with high prediction accuracy. First, the raw hydrological data of cascaded channels are augmented using spatial and temporal windows, which produces data sets with high-dimensional features. Then, Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN) are adopted to build the water level prediction model with the help of the augmented data containing the implicit correlation among multiple channels in spatial dimension and multiple data records in temporal dimension. China’s South-to-North Water Diversion Project is taken as the case study. Experimental results show that our models outperform Support Vector Machine (SVM) by 34.78%, 44.53%, 1.32% and 9.198% in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson Correlation Coefficient (PCC) and Nash’ Sutcliffe Efficiency(NSE), respectively. The accuracies of our models with prediction deviations less than 1 cm, 2 cm, and 3 cm can reach as high as 81.36%, 94.09%, and 97.05%, respectively.