Zhejiang Key Laboratory of Drinking Water Safety and Distribution Technology, Zhejiang University, Hangzhou, 310058, China.
Zhejiang Key Laboratory of Drinking Water Safety and Distribution Technology, Zhejiang University, Hangzhou, 310058, China.
J Environ Manage. 2021 Jul 15;290:112657. doi: 10.1016/j.jenvman.2021.112657. Epub 2021 Apr 20.
Turbidity is an indication of water quality and enables the growth of pathogenic microorganisms. For drinking water treatment plants (DWTPs), violent fluctuations in turbidity are highly disruptive to operational performance due to the lag in process parameter adjustments. Such risks must be carefully managed to guarantee safe drinking water. Machine learning techniques have been proven to be effective for modeling complex nonlinear environmental systems, and this study adopted such a technique to develop a model for predicting source water turbidity for DWTPs to allow DWTPs to make proactive interventions in advance. A random forest (RF) model used preprocessed (empirical mode decomposition and quartile rejecting) meteorological factors (wind speed, wind direction, air temperature, and rainfall) as the input variables, to establish the turbidity prediction of a lake with significant turbidity in China's South Tai Lake. The modeling process included four main stages: (1) source data analysis, (2) raw data preprocessing, (3) modeling and tuning, and (4) model evaluation. The results of the RF model indicated that the correlation coefficient between the predicted and actual sequences is over 0.7, and more than 55% of the predicted values could control the errors within 20% compared to the actual measured values, suggesting that machine learning techniques are suitable for predicting the turbidity of raw source water. It was found that the RF model can provide a modest performance boost because of its stronger capacity to capture nonlinear interactions in the data. The findings of this study can inform the development of turbidity prediction models using readily available meteorological forecast data. The model can be applied to other DWTPs using similar shallow lakes as water sources.
浊度是水质的一个指标，能够促进致病微生物的生长。对于饮用水处理厂（DWTP）而言，由于过程参数调整存在滞后，浊度的剧烈波动会严重干扰其运行性能。为了保证饮用水安全，必须谨慎管理这些风险。机器学习技术已被证明在模拟复杂的非线性环境系统方面非常有效，本研究采用该技术为 DWTP 开发了一个原水浊度预测模型，以便 DWTP 能够提前主动干预。一个随机森林（RF）模型使用预处理（经验模态分解和四分位拒绝）气象因素（风速、风向、气温和降雨量）作为输入变量，对中国太湖地区一个浊度较高的湖泊的浊度进行预测。建模过程包括四个主要阶段：（1）源数据分析，（2）原始数据预处理，（3）建模和调整，以及（4）模型评估。RF 模型的结果表明，预测序列与实际序列之间的相关系数超过 0.7，超过 55%的预测值与实际测量值相比可以控制在 20%以内的误差，这表明机器学习技术适用于预测原水浊度。研究发现，RF 模型可以提供适度的性能提升，因为它具有更强的能力来捕捉数据中的非线性相互作用。本研究的结果可以为使用现成的气象预报数据开发浊度预测模型提供参考。该模型可以应用于其他使用类似浅水湖作为水源的 DWTP。
