integration of conceptual hydrological and machine learning models via output augmentation for enhanced streamflow prediction

Quantifying water resources is essential for developing evidence-based management strategies. hydrological models play a great role in estimating streamflow, particularly in regions with limited flow measurement infrastructure. this study evaluates the integration of the gr4j conceptual hydrological model with machine learning (ml) techniques, random forest (rf), extreme learning machine (elm), extreme gradient boosting (xgb), and long short-term memory (lstm) networks to improve daily streamflow prediction in the bilate river watershed. though gr4j captures general hydrological trends, its limitations in modeling nonlinear dynamics and extreme flows necessitate advanced approaches by augmenting gr4j’s simulated outputs with climate input features to train the ml models. the integrated models gr4j-rf, gr4j-elm, gr4j-xgb, and gr4j-lstm combine gr4j’s physical interpretability with ml’s capability to capture complex and nonlinear relationships, addressing the shortcomings of both the conceptual and ml methods. findings of the study demonstrate significant improvements over standalone gr4j, with gr4j-lstm and gr4j-xgb achieving the highest test performance (nse of 0.77, kge of up to 0.86), gr4j-rf excelling in training fit (train nse of 0.87) with gaps in generalization, and gr4j-elm offering computational efficiency with comparable performance (test nse of 0.74). these findings highlight the potential of integrated modeling to improve streamflow prediction in data-limited regions, supporting applications such as flood prediction and drought monitoring. quantifying water resources is essential for developing evidence-based management strategies. hydrological models play a great role in estimating streamflow, particularly in regions with limited flow measurement infrastructure. this study evaluates the integration of the gr4j conceptual hydrological model with machine learning (ml) techniques, random forest (rf), extreme learning machine (elm), extreme gradient boosting (xgb), and long short-term memory (lstm) networks to improve daily streamflow prediction in the bilate river watershed. though gr4j captures general hydrological trends, its limitations in modeling nonlinear dynamics and extreme flows necessitate advanced approaches by augmenting gr4j’s simulated outputs with climate input features to train the ml models. the integrated models gr4j-rf, gr4j-elm, gr4j-xgb, and gr4j-lstm combine gr4j’s physical interpretability with ml’s capability to capture complex and nonlinear relationships, addressing the shortcomings of both the conceptual and ml methods. findings of the study demonstrate significant improvements over standalone gr4j, with gr4j-lstm and gr4j-xgb achieving the highest test performance (nse of 0.77, kge of up to 0.86), gr4j-rf excelling in training fit (train nse of 0.87) with gaps in generalization, and gr4j-elm offering computational efficiency with comparable performance (test nse of 0.74). these findings highlight the potential of integrated modeling to improve streamflow prediction in data-limited regions, supporting applications such as flood prediction and drought monitoring.