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

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

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中国环境科学 ›› 2026, Vol. 46 ›› Issue (1) : 208-218.

水污染与控制

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

徐小燕¹, 霍冉冉¹, 李惠平², 周佰勤³, 杜振齐⁴, 庞维海², 周婷¹, 董晓晨¹, 李亚仙¹

作者信息 +

An interpretable machine learning-based model for prediction of THMs and identification of key factors in water supply networks

XU Xiao-yan¹, HUO Ran-ran¹, LI Hui-ping², ZHOU Bai-qin³, DU Zhen-qi⁴, PANG Wei-hai², ZHOU Ting¹, DONG Xiao-chen¹, LI Ya-xian¹

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摘要

以苏州某区域供水系统主干管(采样节点间距2km)为研究对象,对干管的水温,余氯,浊度,pH值,UV₂₅₄和溶解性有机碳(DOC)和THMs等多项水质参数沿程变化开展了为期一年的系统性调查研究.空间尺度分析结果表明,供水管网末端8~10km处的THMs浓度处于相对较高水平;时间尺度分析结果表明,供水管网末端THMs的浓度在6月达到最高峰,浓度为39.06μg/L.选择4种典型机器学习算法构建管网THMs预测模型,可解释的梯度提升树(GBDT)算法能够有效避免模型过拟合,对4种THMs预测精度的R²分别达到0.839,0.906,0.836和0.935.进一步应用Shapley附加解释(SHAP)对GBDT模型对全局解析发现,水温,UV₂₅₄,DOC和供水距离与THMs生成呈正相关关系,余氯与THMs呈显著负相关关系;SHAP的局部解析表明,供水管网中余氯浓度低于0.45,0.55和0.55mg/L时可显著降低余氯对TCM,BDCM和DBCM的影响权重.基于本研究构建的数据驱动模型,可通过环境因子高效预测THMs的生成浓度,并实时控制余氯投加量防止夏季或管网末端的THMs过量生成.

Abstract

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 39.06 μg/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.

导出引用

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[J]. 中国环境科学. 2026, 46(1): 208-218

XU Xiao-yan, HUO Ran-ran, LI Hui-ping, ZHOU Bai-qin, DU Zhen-qi, PANG Wei-hai, ZHOU Ting, DONG Xiao-chen, LI Ya-xian. 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

中图分类号： X832

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