Accuracy of HEC-HMS and Artificial Neural Network models in simulating runoffs in upper valley of the Medjerda-Tunisia

The Ghardimaou-Jendouba section of the upper Medjerda valley watershed is located in the extreme north-west of Tunisia. The upstream section of this river has special topographical and hydrographical features that make it particularly vulnerable to flooding. This study aimed to replicate daily streamflow historical records using two distinct modeling approaches: the HEC-HMS and Artificial Neural Network (ANN) models. The effectiveness of both models was rigorously evaluated during their calibration and validation phases using key statistical metrics, namely Root-Mean-Square Error (RMSE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). Model performance varied significantly. Post-validation, the HEC-HMS model yielded R2, NSE, and RMSE values of 0.3668, 0.573, and 0.664, respectively. In contrast, the ANN model ([2−4−1] architecture) showed substantially superior calibration performance: R2 of 0.978, NSE of 0.979, and RMSE of 8.46. These statistics unequivocally point to the ANN model's superior predictive capability. Further analysis revealed HEC-HMS overestimates low flows and underestimates high flows. Conversely, the ANN model accurately estimated both extreme and general flow conditions. This highlights the ANN model's strong potential for precise streamflow forecasting and water resource management in the Ghardimaou-Jendouba Basin. Future studies should compare other advanced machine learning models against HEC-HMS to refine daily streamflow forecasts. • The objective of the current study was to use ANN and HEC-HMS models to simulate daily streamflow data at the Ghardimaou-Jendouba Basin in far northwest Tunisia. • HEC-HMS vs ANN [2-4-1] models: R² (0.664 vs 0.978), NSE (0.573 vs 0.979), RMSE (36.68 vs 8.46). ANN outperforms HEC-HMS. • The ANN model excels in streamflow prediction, unlike HEC-HMS, which overestimates low flows and underestimates high flows. This research informs water policies, flood adaptation, and climate-resilient infrastructure. • To enhance ANN model accuracy, integrating additional meteorological factors is advisable. While the model performs robustly, acknowledging potential limitations and uncertainties inherent in the modeling techniques is crucial for scientific rigor. • Future research should compare alternative machine learning models with HEC-HMS. This advances water resource management by validating complex modeling approaches.