Abstract
Accurate streamflow prediction is crucial for effective water resource management and planning. This study aims to enhance streamflow simulation accuracy in the data-scarce Upper Narmada River Basin (UNB) by proposing a novel hybrid approach, ANN(Hybrid), which combines a physically-based model (WEAP) with a data-driven model (ANN). The WEAP model was calibrated and validated using observed streamflow data, while the ANN model was trained and tested using meteorological variables and simulated streamflow. The ANN(Hybrid) model integrates simulated flow from both WEAP and ANN to improve prediction accuracy. The results demonstrate that the ANN(Hybrid) model outperforms the standalone WEAP and ANN models, with higher NSE values of 95.5% and 92.3% during training and testing periods, respectively, along with an impressive R(2) value of 0.96. The improved streamflow predictions can support better decision-making related to water allocation, reservoir operations, and flood and drought risk assessment. The novelty of this research lies in the development of the ANN(Hybrid) model, which leverages the strengths of both physically-based and data-driven approaches to enhance streamflow simulation accuracy in data-limited regions. The proposed methodology offers a promising tool for sustainable water management strategies in the UNB and other similar catchments.