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, in...

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Bibliographic Details
Published in:Water resources management 2023-10, Vol.37 (13), p.5289-5306
Main Authors: Piraei, Reza, Afzali, Seied Hosein, Niazkar, Majid
Format: Article
Language:English
Subjects:
Additives
Artificial neural networks
Atmospheric Sciences
Civil Engineering
Data points
Earth and Environmental Science
Earth Sciences
Empirical equations
Environment
Gaussian process
Geological surveys
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Hydrology/Water Resources
Loads (forces)
Neural networks
normal distribution
Performance measurement
Rivers
Sediment
Sediment load
Sediments
surveys
water
Water resources
Water resources management
Width
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Summary: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.
ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-023-03606-w