Analysis of Key Influencing Factors of Water Quality in Tai Lake Basin Based on XGBoost-SHAP

Tai Lake Basin, a key freshwater resource in eastern China, has garnered attention due to widespread cyanobacterial blooms. Effective water quality management is vital for the region’s sustainable development. Investigating the seasonal variations of water quality parameters (WQPs) in Tai Lake Basin is essential for devising targeted strategies to enhance water quality. This study employs an interpretable machine learning model (XGBoost-SHAP) to identify the most important factors of water quality using daily monitoring WQP data from 2023 to 2024. Results revealed that dissolved oxygen (DO), total phosphorus (TP), permanganate index (CODMn), and ammonia nitrogen (NH3-N) are primary determinants of water quality in the basin, while water temperature, pH, total nitrogen (TN), and turbidity showed minimal impact (SHAP value < 1). Seasonal analysis demonstrated that DO exerts a substantial influence on water quality during spring, summer, and autumn; TP and CODMn have a stable and negative impact on water quality throughout the year; NH3-N has a relatively significant negative impact on winter water quality. Recommendations include enhancing DO levels in spring and summer, fortifying TP and NH3-N concentrations in winter, and implementing tailored strategies in response to seasonal variations. This research offers valuable insights to guide decision-making processes aimed at enhancing water quality and safeguarding the water environment in the Tai Lake Basin.

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Publication Details

Journal
Water
Published
2025-05-27
DOI
https://doi.org/10.3390/w17111619
Citations
6
Primary Topic
Environmental and Agricultural Sciences
Type
article
Field-Weighted Citation Impact
4.90
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article

Analysis of Key Influencing Factors of Water Quality in Tai Lake Basin Based on XGBoost-SHAP

Qing Cao, Menghua Deng, Weiling Li, Chang Liu
6 citations
Water
Environmental and Agricultural Sciences
4.90
article

Analysis of Key Influencing Factors of Water Quality in Tai Lake Basin Based on XGBoost-SHAP

Qing Cao, Menghua Deng, Weiling Li, Chang Liu
article en
6 citations

Abstract

Tai Lake Basin, a key freshwater resource in eastern China, has garnered attention due to widespread cyanobacterial blooms. Effective water quality management is vital for the region’s sustainable development. Investigating the seasonal variations of water quality parameters (WQPs) in Tai Lake Basin is essential for devising targeted strategies to enhance water quality. This study employs an interpretable machine learning model (XGBoost-SHAP) to identify the most important factors of water quality using daily monitoring WQP data from 2023 to 2024. Results revealed that dissolved oxygen (DO), total phosphorus (TP), permanganate index (CODMn), and ammonia nitrogen (NH3-N) are primary determinants of water quality in the basin, while water temperature, pH, total nitrogen (TN), and turbidity showed minimal impact (SHAP value < 1). Seasonal analysis demonstrated that DO exerts a substantial influence on water quality during spring, summer, and autumn; TP and CODMn have a stable and negative impact on water quality throughout the year; NH3-N has a relatively significant negative impact on winter water quality. Recommendations include enhancing DO levels in spring and summer, fortifying TP and NH3-N concentrations in winter, and implementing tailored strategies in response to seasonal variations. This research offers valuable insights to guide decision-making processes aimed at enhancing water quality and safeguarding the water environment in the Tai Lake Basin.

WaterVol. 17(11)
Hohai University (CN), Nanjing University of Information Science and Technology (CN)
Openalex Percentile: Top 5%
Environmental and Agricultural Sciences
4.90
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Analysis of Key Influencing Factors of Water Quality in Tai Lake Basin Based on XGBoost-SHAP — Qing Cao, Menghua Deng, et al. · Water (2025) | TGRS Research Map | TGRS