Integrating SWAT and machine learning for streamflow simulation and runoff sensitivity in the Tawi watershed
Comparative analysis indicates that SWAT provides physically interpretable and consistent simulations of streamflow dynamics, whereas machine learning models, particularly XGBoost and RF, offer efficient data-driven predictions with strong capability in capturing nonlinear relationships.