Enhancing reservoir inflow forecasting precision through Bayesian Neural Network modeling and atmospheric teleconnection pattern analysis

Via the framework of this research, a Bayesian Neural Network (BNN) machine learning model integrated with atmospheric teleconnection patterns was employed to predict the monthly inflow to three major dams (Zayanderood, Amirkabir, and Karun 3) in Iran. The impact of eight teleconnection indices throughout 1 to 12 months, as well as local station variables such as precipitation and inflow, was assessed. Optimal input variables and time delays were determined utilizing the Mutual Information index, identifying specific teleconnection patterns as significant influencers on dam inflow. The performance of the BNN model was compared to an Artificial Neural Network (ANN) model using both deterministic and probabilistic metrics. For deterministic evaluation, the Normalized Root Mean Square Error (NRMSE) for the BNN model in the best prediction scenarios was 10.93%, 9.07%, and 7.55% for Zayanderood, Amirkabir, and Karun 3 dams, respectively. The corresponding values for the ANN model were 12.27%, 10.72%, and 7.71%. Additionally, probabilistic evaluation using CRPS demonstrated that BNN outperformed ANN in the test phase, with CRPS values of 8.98 m3/s compared to 14.69 m3/s (Zayanderood), 1.77 m3/s compared to 3.17 m3/s (Amirkabir), and 36.16 m3/s compared to 51.22 m3/s (Karun 3), highlighting BNN's superior predictive skill. Despite these results, both models exhibited limitations in accurately predicting inflow peaks. This study highlights the potential of teleconnection patterns as predictive variables for dam inflow and underscores the importance of further exploration across different regions. Using BNN for dam inflow prediction is a significant contribution to the field of hydrological forecasting and offers a generalizable approach for incorporating large-scale climate patterns into water resources management.