Comparative analysis of SWAT and SWAT coupled with XGBoost model using Optuna hyperparameter optimization for nutrient simulation: A case study in the Upper Nan River basin, Thailand

Agricultural runoff leading to nitrate (NO3-N) and orthophosphate (PO4-P) contamination poses significant environmental and public health risks. This study integrates the Soil and Water Assessment Tool (SWAT) with eXtreme Gradient Boosting (XGBoost), optimized using Optuna hyperparameter tuning, to...

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Vydáno v:Journal of environmental management Ročník 388; s. 126053
Hlavní autoři: Pinichka, Chayut, Chotpantarat, Srilert, Cho, Kyung Hwa, Siriwong, Wattasit
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Elsevier Ltd 01.07.2025
Témata:
Agriculture
Boosting Machine Learning Algorithms
Environmental Monitoring - methods
Machine Learning
Nitrates
Nutrient simulation
Nutrients
Optuna
Rivers - chemistry
Soil - chemistry
SWAT
Thailand
Water Pollutants, Chemical - analysis
XGBoost
Thailand
SWAT
Optuna
Thailand
Nutrient simulation
XGBoost
Machine learning
ISSN:0301-4797, 1095-8630, 1095-8630
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Shrnutí:Agricultural runoff leading to nitrate (NO3-N) and orthophosphate (PO4-P) contamination poses significant environmental and public health risks. This study integrates the Soil and Water Assessment Tool (SWAT) with eXtreme Gradient Boosting (XGBoost), optimized using Optuna hyperparameter tuning, to enhance predictions of nutrient concentrations in water bodies. The hybrid SWAT-XGBoost model combines physical hydrological processes with machine learning for improved accuracy. The optimized model demonstrated significant improvements over SWAT alone, with R2 values increasing by up to 50 % and RMSE decreasing by up to 75 % across datasets. These results highlight the enhanced predictive capabilities of the hybrid approach in capturing nutrient transport dynamics. SHAP analysis further identified key factors, such as sediment dynamics and nutrient mineralization, as dominant drivers of contamination, providing actionable insights for effective watershed management. By integrating SHAP analysis, the study identified key processes influencing nutrient transport, such as sediment dynamics and nutrient mineralization, offering deeper insights into pollution pathways. This approach provides a scalable and adaptable framework for improving watershed management, supporting sustainable agricultural practices, and mitigating contamination risks. The findings highlight an approach that combines physical modeling with machine learning to effectively address complex environmental challenges. [Display omitted] •SWAT-XGBoost hybrid model improves nutrient prediction accuracy.•Optuna improves SWAT-XGBoost performance for nutrient modeling.•SHAP enhances interpretability of nutrient prediction drivers.•Significant gains in R2, RMSE, and NSE for prediction of nitrate and phosphate.•Results inform key drivers of nutrient and watershed management.
Bibliografie:ObjectType-Article-1
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ISSN:0301-4797
1095-8630
1095-8630
DOI:10.1016/j.jenvman.2025.126053