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A Hybrid Gradient Boosting Model for Predicting Longitudinal Dispersion Coefficient in Natural Rivers

Author

Listed:
  • Yangyu Deng

    (Dalian University of Technology)

  • Yakun Liu

    (Dalian University of Technology)

  • Di Zhang

    (Dalian University of Technology)

  • Ze Cao

    (Dalian University of Technology)

Abstract

Precise estimation of the longitudinal dispersion coefficient (Kx​) is essential for modeling pollutant transport in rivers. To address the limitations of existing empirical formulas for Kx​​ calculation, this study introduces a hybrid machine learning model, SSA-CatBoost, which combines the Sparrow Search Algorithm (SSA) with the CatBoost framework to enhance hyperparameter optimization. Its performance is benchmarked against popular gradient boosting models such as CatBoost, XGBoost and GBDT. Results demonstrate that the newly developed SSA-CatBoost gives better comprehensive prediction performances than other gradient boosting models with RMSE of 878.02 and MAE of 478.88 for dimensionless Kx and RMSE of 163.15 m2/s and MAE of 82.44 m2/s for dimensioned Kx. Furthermore, comparisons with traditional empirical formulas highlight the enhanced precision of SSA-CatBoost. Besides, the uncertainty analysis also suggests that SSA-CatBoost is able to give higher prediction reliability with the uncertainty bandwidth of 584 for dimensionless Kx and 95 m2/s for dimensioned Kx. These findings establish SSA-CatBoost as a reliable and effective solution for Kx​ prediction in natural river systems.

Suggested Citation

  • Yangyu Deng & Yakun Liu & Di Zhang & Ze Cao, 2025. "A Hybrid Gradient Boosting Model for Predicting Longitudinal Dispersion Coefficient in Natural Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(5), pages 2111-2131, March.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:5:d:10.1007_s11269-024-04058-6
    DOI: 10.1007/s11269-024-04058-6
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    References listed on IDEAS

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    1. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Zeng, Wenzhi & Wang, Xiukang & Zou, Haiyang, 2019. "Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 186-212.
    2. Xiangtao Li & Huawen Liu & Minghao Yin, 2013. "Differential Evolution for Prediction of Longitudinal Dispersion Coefficients in Natural Streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(15), pages 5245-5260, December.
    3. Hazi Azamathulla & Aminuddin Ghani, 2011. "Genetic Programming for Predicting Longitudinal Dispersion Coefficients in Streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(6), pages 1537-1544, April.
    4. Bulent Tutmez & Mehmet Yuceer, 2013. "Regression Kriging Analysis for Longitudinal Dispersion Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3307-3318, July.
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