Assessment of XGBoost to Estimate Total Sediment Loads in Rivers

Estimation of total sediment loads is a significant topic in river management as direct measurement is costly and time-consuming. This study aims not only to use the eXtreme Gradient Boosting (XGBoost) model but also to compare its performance with that of other empirical equations and ML models, including Artificial Neural Networks (ANN), AdaBoost, Gradient Boost Regressor, Random Forest Regressor, and Gaussian Process. 543 data points from the United States Geological Survey were used to train and test different models. The results showed that XGBoost outperformed other methods considering six performance metrics. To be more specific, the root mean square errors and determination coefficient were 216 and 0.95, respectively, whereas the corresponding metrics for ANN were 316.23 and 0.87, respectively. To interpret the sediment predictions and delineate the importance of each feature, XGBoost feature importance and SHapley Additive exPlanations (SHAP) were utilized. According to the feature importance analysis, estimations of the XGBoost model was mostly (72%) affected by the water surface width. Moreover, SHAP analysis verified the importance of water surface width on the final predictions. Finally, based on the results achieved in this study, further applications of XGBoost in water resources management are postulated.