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Prediction of effluent water quality and analysis of influencing factors in constructed wetlands based on machine learning
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Ya-song CHEN1, Jia-wen LIU2, Yun-peng ZHAO1, Ying-ping ZHOU2, Qiu-shi SHEN1, Lin XIAO2, *, Xin QIAN2
China Environmental Science | 2025, 45(6) : 3161 - 3170
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China Environmental Science | 2025, 45(6): 3161-3170
• Water Pollution Control •
Prediction of effluent water quality and analysis of influencing factors in constructed wetlands based on machine learning
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Ya-song CHEN1, Jia-wen LIU2, Yun-peng ZHAO1, Ying-ping ZHOU2, Qiu-shi SHEN1, Lin XIAO2, *, Xin QIAN2
Affiliations
  • 1.National Engineering Research Center of Eco-Environment in the Yangtze River Economic Belt, China Three Gorges, Wu Han 430010, China
  • 2.State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
Published: 2025-06-20
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Based on water quality indicators, climate indicators, and wetland operation parameters, data from previous studies were collected to predict the effluent concentrations of ammonia nitrogen (NH4+-N), COD, sulfamethoxazole (SMX), and some heavy metals in constructed wetlands using three machine learning models. The results showed that the Random Forest model slightly outperformed XGBoost and LightGBM in overall performance, demonstrating more stable R2 and RMSE values. In particular, it achieved higher accuracy in predicting NH4+-N and SMX concentrations, with R2 values of 0.93, 0.89, and 0.87, respectively, for NH4+-N. In contrast, the models performed relatively weaker in COD predictions, with R2 values of 0.71, 0.61, and 0.64, respectively. By incorporating the SMOTE data augmentation technique, the prediction performance and accuracy of the models were significantly enhanced, especially for COD, where improvements ranged from 7.04% to 26.23%. This study combines scientific data analysis with machine learning algorithms, providing a feasible approach for practical engineering applications.

machine learning  /  constructed wetland  /  ammonium  /  COD  /  heavy metal
Ya-song CHEN, Jia-wen LIU, Yun-peng ZHAO, Ying-ping ZHOU, Qiu-shi SHEN, Lin XIAO, Xin QIAN. Prediction of effluent water quality and analysis of influencing factors in constructed wetlands based on machine learning[J]. China Environmental Science, 2025 , 45 (6) : 3161 -3170 .
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Year 2025 volume 45 Issue 6
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Article Info
  • Receive Date：2024-11-02
  • Online Date：2026-02-27
  • Published：2025-06-20
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  • Received：2024-11-02
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Affiliations
    1.National Engineering Research Center of Eco-Environment in the Yangtze River Economic Belt, China Three Gorges, Wu Han 430010, China
    2.State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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