基于Optuna超参数优化的SWAT与SWAT耦合XGBoost模型在泰国南河上游流域养分模拟中的对比分析

IF 8.4 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Chayut Pinichka , Srilert Chotpantarat , Kyung Hwa Cho , Wattasit Siriwong
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引用次数: 0

摘要

导致硝酸盐(NO3-N)和正磷酸盐(PO4-P)污染的农业径流构成了重大的环境和公共健康风险。本研究将土壤和水分评估工具(SWAT)与使用Optuna超参数优化的极端梯度增强(XGBoost)相结合,以增强水体中养分浓度的预测。混合SWAT-XGBoost模型将物理水文过程与机器学习相结合,以提高精度。与单独的SWAT相比,优化模型显示出显著的改进,跨数据集的R2值增加了高达50%,RMSE降低了高达75%。这些结果突出了杂交方法在捕获养分运输动力学方面的增强预测能力。SHAP分析进一步确定了关键因素,如泥沙动力学和养分矿化,是污染的主要驱动因素,为有效的流域管理提供了可行的见解。通过整合SHAP分析,该研究确定了影响养分运输的关键过程,如沉积物动力学和养分矿化,为污染途径提供了更深入的见解。这种方法为改善流域管理、支持可持续农业做法和减轻污染风险提供了可扩展和适应性强的框架。研究结果强调了一种将物理建模与机器学习相结合的方法,可以有效地应对复杂的环境挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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 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.
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来源期刊
Journal of Environmental Management
Journal of Environmental Management 环境科学-环境科学
CiteScore
13.70
自引率
5.70%
发文量
2477
审稿时长
84 days
期刊介绍: The Journal of Environmental Management is a journal for the publication of peer reviewed, original research for all aspects of management and the managed use of the environment, both natural and man-made.Critical review articles are also welcome; submission of these is strongly encouraged.
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