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Assessment of XGBoost to Estimate Total Sediment Loads in Rivers

Author

Listed:
  • Reza Piraei

    (Shiraz University)

  • Seied Hosein Afzali

    (Shiraz University)

  • Majid Niazkar

    (Free University of Bozen-Bolzano)

Abstract

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.

Suggested Citation

  • Reza Piraei & Seied Hosein Afzali & Majid Niazkar, 2023. "Assessment of XGBoost to Estimate Total Sediment Loads in Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(13), pages 5289-5306, October.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:13:d:10.1007_s11269-023-03606-w
    DOI: 10.1007/s11269-023-03606-w
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    References listed on IDEAS

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    1. Javad Zahiri & Zeynab Mollaee & Mohammad Reza Ansari, 2020. "Estimation of Suspended Sediment Concentration by M5 Model Tree Based on Hydrological and Moderate Resolution Imaging Spectroradiometer (MODIS) Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(12), pages 3725-3737, September.
    2. Elham Ghanbari-Adivi & Mohammad Ehteram & Alireza Farrokhi & Zohreh Sheikh Khozani, 2022. "Combining Radial Basis Function Neural Network Models and Inclusive Multiple Models for Predicting Suspended Sediment Loads," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4313-4342, September.
    3. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
    4. Hamid Moeeni & Hossein Bonakdari, 2018. "Impact of Normalization and Input on ARMAX-ANN Model Performance in Suspended Sediment Load Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 845-863, February.
    5. Ashish Kumar & Pravendra Kumar & Vijay Kumar Singh, 2019. "Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1217-1231, February.
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    Cited by:

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    5. Anurag Barthwal & Mamta Bhatt & Shwetank Avikal & Chandra Prakash, 2025. "Machine learning-based prediction models for electoral outcomes in India: a comparative analysis of exit polls from 2014–2021," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(1), pages 313-338, February.

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