8/9/2023 0 Comments Neural network model of memory![]() ![]() However, because of the nonlinear behaviour of the streamflow time series, streamflow prediction remains one of the very difficult matters in the field of hydrological sciences 2, 3. Thus, CNN-LSTM has significant practical value in Q flow prediction.Īccurate streamflow ( Q flow) prediction is crucial for efficient water management tasks, such as improving the efficiency of hydroelectricity generation, irrigation planning and flood management 1. In summary, the results reveal that the proposed CNN-LSTM model based on the novel framework yields more accurate predictions. 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. 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. Q flow prediction is conducted for different time intervals with the length of 1-Week, 2-Weeks, 4-Weeks, and 9-Months, respectively. 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. ![]() 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. 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. It is highly vital for hydropower operation, agricultural planning, and flood control. Streamflow ( Q flow) prediction is one of the essential steps for the reliable and robust water resources planning and management. ![]()
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