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

• Published in: Journal of hydrology (Amsterdam) Vol. 585; p. 124783
• Main Authors: Ren, Tao, Liu, Xuefeng, Niu, Jianwei, Lei, Xiaohui, Zhang, Zhao
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2020
• Subjects: case studies; China; data collection; hydrologic data; Multilayer percetion; neural networks; prediction; Recurrent neural network; South-to-North Water Diversion Project; support vector machines; Water level prediction; China; Multilayer percetion; Water level prediction; South-to-North Water Diversion Project; Recurrent neural network
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2020.124783

•Adopting neural networks to predict channel water level can improve accuracies.•Features of multiple channels and time-slices are helpful for water level prediction.•The prediction model based on recurrent neural network is more effective.•The accuracy of recurrent neural network based model reaches up to 97.05%. 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.