Suspended sediment load (Qs) is an important parameter in the analysis of hydrological processes and management of water resources. Direct methods of measuring Qs are costly and require precise instruments, which makes their application limited, especially in remote regions. Indirect methods, on the other hand, discover the relationships between river hydrological parameters and Qs. Machine learning-based models are among the empirical data mining approaches that have been employed for the prediction of Qs under various conditions. Ensemble models, e.g., XGBoost (Python 3.12.3 with XGBoost version 3.1.0), are among the widely used machine learning approaches in the hydrologic context. A challenging step in establishing such models is conducting suitable hyperparameter tuning. A modeling study is reported here that combines the metaheuristic red fox algorithm (RFO) with XGBoost to improve Qs prediction. Daily observations of 21 years from Illinois State, USA (12 rivers), were used to assess the proposed methodology. Hydrologic data, including water stage, temperature, sediment concentration and river water flowrate were used as input variables when defining two input configurations. The obtained results reveal that the proposed RFO-XGBoost model outperformed the standalone XGBoost model in all the studied sites for both input configurations. However, the performance improvement percentage fluctuated among the sites. It was found that the model improvement was primarily affected by river hydrologic characteristics. A SHAP analysis revealed river flowrate as the most empirically influential input parameter in the model’s predictions of Qs. Uncertainty analysis through the Monte Carlo simulations further confirmed the proposed model’s enhanced performance and robustness.
Sadeghzadeh et al. (2026) studied this question.