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An interpretable machine learning-based model for prediction of THMs and identification of key factors in water supply networks
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Xiao-yan XU1, Ran-ran HUO1, Hui-ping LI2, *, Bai-qin ZHOU3, Zhen-qi DU4, Wei-hai PANG2, Ting ZHOU1, Xiao-chen DONG1, Ya-xian LI1
China Environmental Science | 2026, 46(1) : 208 - 218
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China Environmental Science | 2026, 46(1): 208-218
• Water Pollution Control •
An interpretable machine learning-based model for prediction of THMs and identification of key factors in water supply networks
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Xiao-yan XU1, Ran-ran HUO1, Hui-ping LI2, *, Bai-qin ZHOU3, Zhen-qi DU4, Wei-hai PANG2, Ting ZHOU1, Xiao-chen DONG1, Ya-xian LI1
Affiliations
  • 1.Suzhou Industrial Park Qingyuan Hong Kong & China Water Co. Ltd., Suzhou 215021, China
  • 2.School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
  • 3.School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
  • 4.School of Municipal and Environmental Engineering, Shandong University of Architecture, Jinan 250101, China
Published: 2026-01-20
Outline
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A one-year study was carried out to investigate the changes in water quality parameters such as water temperature, residual chlorine, turbidity, pH, UV254, dissolved organic carbon (DOC) and trihalomethanes(THMs) along the main pipe of a regional water supply system in Suzhou (2km sampling interval). The results of spatial scale analysis showed that the concentration of THMs at the end of the water supply pipe network at 8~10km was at a relatively high level; the results of time scale analysis showed that the concentration of THMs at the end of the water supply pipe network reaches the highest peak in June, with the concentration of 39.06μg/L. Four typical machine learning algorithms were selected to construct the prediction model of THMs in the pipe network, and the interpretable gradient boosting tree (GBDT) algorithm was able to effectively avoid model overfitting, and the R2 of the four THMs prediction accuracies reached 0.839, 0.906, 0.836, and 0.935, respectively. The further application of Shapley's additional explanation (SHAP) to the global analysis of the GBDT model revealed that the water temperature, UV254, DOC, and the distance of the water supply were positively correlated with the generation of THMs, while the residual chlorine showed a significant negative correlation with the THMs. The local analysis of SHAP showed that residual chlorine concentration in the water supply network lower than 0.45, 0.55 and 0.55mg/L can significantly reduce the influence weights of residual chlorine on TCM, BDCM and DBCM. Based on the data-driven model constructed in this study, the generation concentration of THMs can be efficiently predicted by environmental factors, and the residual chlorine dosage can be controlled in real time to prevent the over-generation of THMs in summer or at the end of the pipe network.

water supply network  /  residual chlorine  /  distance to water supply  /  THMs  /  machine learning  /  SHAP analysis
徐小燕, 霍冉冉, 李惠平, 周佰勤, 杜振齐, 庞维海, 周婷, 董晓晨, 李亚仙. 基于可解释机器学习的供水管网三卤甲烷浓度预测及关键驱动因子识别模型. 中国环境科学, 2026 , 46 (1) : 208 -218 .
Xiao-yan XU, Ran-ran HUO, Hui-ping LI, Bai-qin ZHOU, Zhen-qi DU, Wei-hai PANG, Ting ZHOU, Xiao-chen DONG, Ya-xian LI. An interpretable machine learning-based model for prediction of THMs and identification of key factors in water supply networks[J]. China Environmental Science, 2026 , 46 (1) : 208 -218 .
Year 2026 volume 46 Issue 1
65
13
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  • Receive Date:2025-05-19
  • Online Date:2026-10-08
  • Published:2026-01-20
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  • Received:2025-05-19
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Affiliations
    1.Suzhou Industrial Park Qingyuan Hong Kong & China Water Co. Ltd., Suzhou 215021, China
    2.School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
    3.School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
    4.School of Municipal and Environmental Engineering, Shandong University of Architecture, Jinan 250101, 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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