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

Yangyu Deng, Yakun Liu (), Di Zhang and Ze Cao
Additional contact information
Yangyu Deng: Dalian University of Technology
Yakun Liu: Dalian University of Technology
Di Zhang: Dalian University of Technology
Ze Cao: Dalian University of Technology

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 5, No 9, 2131 pages

Abstract: 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.

Keywords: Longitudinal Dispersion Coefficient; Natural Rivers; Gradient Boosting Model; CatBoost; Sparrow Search Algorithm (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-024-04058-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:39:y:2025:i:5:d:10.1007_s11269-024-04058-6

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-024-04058-6

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().