Forecasting bacteriological presence in treated drinking water using machine learning
Forecasting bacteriological presence in treated drinking water using machine learning
复制标题
DOI：
10.3389/frwa.2023.1199632
复制
发表时间：
2023-06
期刊：
影响因子：
--
通讯作者：
Grigorios Kyritsakas;J. Boxall;V. Speight
中科院分区：
文献类型：
--
作者：
Grigorios Kyritsakas;J. Boxall;V. Speight
提出了一种新的数据驱动模型，用于以细胞总数的形式预测饮用水处理厂处理后的水中细菌的存在。该模型是使用一家运行中的饮用水处理厂的一年每小时在线流式细胞仪数据开发和验证的。比较了各种机器学习方法(随机森林、支持向量机、k近邻、前馈人工神经网络、长短期记忆和RusBoost)，并采用了不同的变量选择方法来提高模型的精度。结果表明，对于回归预测和基于分类的预测，该模型都能准确地预测12h前的细胞总数--使用K-近邻算法的最佳回归模型的NSE=0.96，使用组合随机森林、K-邻居和RusBoost算法的最佳分类模型的准确率=89.33%。这一预测范围足以使积极的业务干预措施改善处理过程，从而有助于确保安全饮用水。
A novel data-driven model for the prediction of bacteriological presence, in the form of total cell counts, in treated water exiting drinking water treatment plants is presented. The model was developed and validated using a year of hourly online flow cytometer data from an operational drinking water treatment plant. Various machine learning methods are compared (random forest, support vector machines, k-Nearest Neighbors, Feed-forward Artificial Neural Network, Long Short Term Memory and RusBoost) and different variables selection approaches are used to improve the model's accuracy. Results indicate that the model could accurately predict total cell counts 12 h ahead for both regression and classification-based forecasts—NSE = 0.96 for the best regression model, using the K-Nearest Neighbors algorithm, and Accuracy = 89.33% for the best classification model, using the combined random forest, K-neighbors and RusBoost algorithms. This forecasting horizon is sufficient to enable proactive operational interventions to improve the treatment processes, thereby helping to ensure safe drinking water.