Bayesian Optimized CNN-RNN Hybrid Model for Predicting Streamflow in Potomac River Basin

Accurately estimating streamflow quantity within a watershed is essential for anticipating potential severe drought conditions in the future. This paper introduces a hybrid model that combines Convolutional Neural Networks and Recurrent Neural Networks (CNN-RNN) to predict streamflow discharge using historical data from the Potomac River Basin. The experimental outcomes showcase the model's proficiency in successfully predicting streamflow discharge values for the upcoming month. Additionally, a new sequence, derived from a one-step-ahead approach, is constructed to forecast streamflow discharge values beyond the testing data horizon. Notably, the CNN-RNN model demonstrates superior performance with a lower Symmetric Mean Absolute Percentage Error (sMAPE) at 0.048592 compared to the RNN method at 0.067662. Moreover, the prediction accuracy of the CNN-RNN model surpasses that of the RNN model. The correlation between the test sequence and the CNN-RNN prediction is notably high at 0.97753, while the RNN correlation lags slightly at 0.96694. Furthermore, the results affirm the CNN-RNN model's potential capability to forecast unobserved values beyond the time series horizon when observed data is unavailable.