Machine Learning Based Water Quality Evolution and Pollution Identification in Reservoir Type Rivers
1School of Smart City, Chongqing Jiaotong University, No.66 Xuefu Rd., Nan'an Dist, Chongqing, 400074, China; Technology Innovation Center for Spatio-temporal Information and Equipment of Intelligent City, Ministry of Natural Resources, No.6 Qingzhu East Road, Yubei Dist., Chongqing, 401121, China.
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Summary
Reservoir regulation significantly impacts river water quality, increasing chlorophyll-a but moderately affecting total nitrogen. Machine learning, particularly XGBoost, accurately monitors these pollutant dynamics for ecosystem management.
Area of Science:
- Environmental Science
- Water Resource Management
- Remote Sensing
- Machine Learning Applications
Background:
- Quantifying pollutant transport and transformation in reservoir-regulated rivers is challenging due to complex hydrodynamic and biogeochemical interactions.
- Understanding these dynamics is crucial for managing water quality in vital river systems like the Yulin River, a tributary of the Three Gorges Reservoir.
Purpose of the Study:
- To explore the spatiotemporal dynamics of water quality parameters (WQPs) in the Yulin River using field monitoring and satellite imagery.
- To evaluate the performance of advanced machine learning algorithms for retrieving WQPs, including chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), and chlorophyll-a (Chla).
- To assess the influence of hydrological regulation and meteorological factors on pollutant dynamics.
Main Methods:
- Combined 48 months of high-frequency field monitoring data (January 2020 to December 2023) with Sentinel-2 multispectral imagery.
- Systematically evaluated four machine learning algorithms: Extreme Gradient Boosting (XGBoost), Random Forest (RF), Categorical Boosting (CatBoost), and Gradient Boosted Decision Trees (GBDT).
- Analyzed the impact of reservoir impoundment and hydrological conditions (low-flow vs. high-flow) on WQPs.
Main Results:
- XGBoost demonstrated superior performance in WQP retrieval, with R² values from 0.9154 to 0.9488 and low RMSE.
- Reservoir impoundment caused significant Chla surges (100%-1000% increase in 56.2% of the area) and moderate TN fluctuations (≤40% growth in 73% of the area).
- Hydrological conditions strongly influenced COD and Chla in estuarine regions, with higher concentrations during low-flow periods. Meteorological factors showed weak correlations (|r| < 0.41).
Conclusions:
- XGBoost is a robust tool for large-scale WQP retrieval and watershed-scale monitoring, offering high precision (MAE 0.0201-1.4277 mg/L).
- Hydrological regulation, particularly reservoir impoundment, is the predominant driver of pollutant dynamics in the Yulin River.
- The findings provide critical insights for effective management of river ecosystems influenced by reservoir operations.