Centre de Recherche en Aménagement et Développement (CRAD), Université Laval, 2325 Allée des Bibliothèques, Québec City, QC, G1V 0A6, Canada.
Centre de Recherche en Aménagement et Développement (CRAD), Université Laval, 2325 Allée des Bibliothèques, Québec City, QC, G1V 0A6, Canada.
J Environ Manage. 2024 Jun;362:121378. doi: 10.1016/j.jenvman.2024.121378. Epub 2024 Jun 5.
Source and raw water quality may deteriorate due to rainfall and river flow events that occur in watersheds. The effects on raw water quality are normally detected in drinking water treatment plants (DWTPs) with a time-lag after these events in the watersheds. Early warning systems (EWSs) in DWTPs require models with high accuracy in order to anticipate changes in raw water quality parameters. Ensemble machine learning (EML) techniques have recently been used for water quality modeling to improve accuracy and decrease variance in the outcomes. We used three decision-tree-based EML models (random forest [RF], gradient boosting [GB], and eXtreme Gradient Boosting [XGB]) to predict two critical parameters for DWTPs, raw water Turbidity and UV absorbance (UV254), using rainfall and river flow time series as predictors. When modeling raw water turbidity, the three EML models (r=0.87, r=0.80 and r=0.81) showed very good performance metrics. For raw water UV254, the three models (r=0.89, r=0.85 and r=0.88) again showed very good performance metrics. Results from this study suggest that EML approaches could be used in EWSs to anticipate changes in the quality parameters of raw water and enhance decision-making in DWTPs.
由于流域内的降雨和河流流量事件，水源和原水水质可能会恶化。这些事件发生在流域后，通常会在饮用水处理厂（DWTP）中检测到对原水水质的影响。DWTP 中的预警系统（EWS）需要具有高精度的模型，以便预测原水水质参数的变化。最近，集合机器学习（EML）技术已被用于水质建模，以提高准确性并降低结果的方差。我们使用了三种基于决策树的 EML 模型（随机森林[RF]、梯度提升[GB]和极端梯度提升[XGB]），使用降雨和河流流量时间序列作为预测因子，来预测 DWTP 的两个关键参数，原水浊度和紫外线吸收率（UV254）。在对原水浊度进行建模时，三个 EML 模型（r=0.87、r=0.80 和 r=0.81）表现出非常好的性能指标。对于原水 UV254，三个模型（r=0.89、r=0.85 和 r=0.88）再次表现出非常好的性能指标。本研究结果表明，EML 方法可用于 EWS 以预测原水水质参数的变化，并增强 DWTP 的决策能力。
