Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks

Ghimire, Sujan; Yaseen, Zaher Mundher; Farooque, Aitazaz A.; Deo, Ravinesh C.; Zhang, Ji; Tao, Xiaohui

Streamflow (Q(flow)) prediction is one of the essential steps for the reliable and robust water resources planning and management. It is highly vital for hydropower operation, agricultural planning, and flood control. In this study, the convolution neural network (CNN) and Long-Short-term Memory network (LSTM) are combined to make a new integrated model called CNN-LSTM to predict the hourly Q(flow) (short-term) at Brisbane River and Teewah Creek, Australia. The CNN layers were used to extract the features of Q(flow) time-series, while the LSTM networks use these features from CNN for Q(flow) time series prediction. The proposed CNN-LSTM model is benchmarked against the standalone model CNN, LSTM, and Deep Neural Network models and several conventional artificial intelligence (AI) models. Q(flow) prediction is conducted for different time intervals with the length of 1-Week, 2-Weeks, 4-Weeks, and 9-Months, respectively. With the help of different performance metrics and graphical analysis visualization, the experimental results reveal that with small residual error between the actual and predicted Q(flow), the CNN-LSTM model outperforms all the benchmarked conventional AI models as well as ensemble models for all the time intervals. With 84% of Q(flow) prediction error below the range of 0.05 m(3) s(−1), CNN-LSTM demonstrates a better performance compared to 80% and 66% for LSTM and DNN, respectively. In summary, the results reveal that the proposed CNN-LSTM model based on the novel framework yields more accurate predictions. Thus, CNN-LSTM has significant practical value in Q(flow) prediction.