[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122665-en":3,"doc-seo-122665-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122665,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Forecasting Bacteriological Presence in Treated Drinking Water Using Machine Learning","A novel data-driven model predicts bacteriological presence in treated drinking water by estimating total cell counts at the exit of drinking water treatment plants. The model is trained and validated with one year of hourly online flow cytometer data collected from an operational facility. Multiple machine learning approaches are benchmarked, including random forest, support vector machines, k-nearest neighbors, feed-forward artificial neural networks, long short-term memory, and RusBoost, with feature-selection strategies used to enhance accuracy. Results support reliable 12-hour-ahead forecasts for both regression and classification tasks, enabling proactive operational interventions to improve treatment processes and help safeguard drinking water quality.","TYPE Original Research PUBLISHED 30 June 2023  \nDOI 10. 3389/frwa.2023.1199632  \nOPEN ACCESS  \nEDITED BY  \nIbrahim Demir,  \nThe University of Iowa, United States  \nREVIEWED BY  \nMohammad Najafzadeh, Graduate University of Advanced Technology, Iran  \nUsman T. Khan,  \nYork University, Canada  \n*CORRESPONDENCE  \nGrigorios Kyritsakas  \n g. kyritsakas@she􀀈eld.ac. uk  \nRECEIVED 10 April 2023  \nACCEPTED 16 June 2023  \nPUBLISHED 30 June 2023  \nCITATION  \nKyritsakas G, Boxall J and Speight V (2023) Forecasting bacteriological presence in treated drinking water using machine learning.  \nFront. Water 5:1199632 .  \ndoi: 10.3389/frwa.2023.1199632  \nCOPYRIGHT  \n© 2023 Kyritsakas, Boxall and Speight. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nForecasting bacteriological presence in treated drinking water using machine learning  \nGrigorios Kyritsakas*, Joby Boxall and Vanessa Speight  \nDepartment of Civil and Structural Engineering, She􀀈eld Water Centre, The University of She􀀈eld, She􀀈eld, United Kingdom  \nA 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 di􀀀erent variables selection approaches are used to improve the model’s accuracy. Results indicate that the model could accurately predict total cell counts 12h 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 su􀀈cient to enable proactive operational interventions to improve the treatment processes, thereby helping to ensure safe drinking water.  \nKEYWORDS  \ndrinking water treatment, machine learning, online flow cytometry, total cell counts prediction, forecasting model  \n1. Introduction  \nDrinking water treatment plants (DWTPs) are complicated systems tasked with processing raw water to produce high-quality drinking water that complies with the regulatory standards. The treatment process consists of di􀀓erent steps from pre-treatment to disinfection that are monitored with sensors connected to the supervisory control and data acquisition (SCADA) system. The SCADA systems o􀀓er a real-time check of the 􀀃ow entering and exiting the various treatment stages, the quality of the water exiting each treatment stage, the quality of the treated water exiting the DWTP, and the condition and the operational status (on/o􀀓) of the electromechanical equipment (pumps, valves, chemical mixers etc.) and the water level of any tanks. Drinking water quality is monitored through SCADA via the sensor detection of the key water quality indicator variables including pH, turbidity, and disinfectant residual with a frequency between 5 to 15min. Moreover, samples are collected with a daily frequency at the DWTPs outlet to measure key water quality parameters such as coliform bacteria, heterotrophic plate counts, chlorine and turbidity (DWI, 2020) .  \nDWTP sta􀀓 use the collected data to adapt the treatment processes to address changes in the 􀀃ow or the quality of the water entering the plant. These interventions in the treatm","cbCair9bzxbUFb68","https://ap.wps.com/l/cbCair9bzxbUFb68","pdf",1042178,1,15,"English","en",105,"# Introduction\n## Application of machine learning for DWTP management","[{\"question\":\"What does the proposed model forecast in treated drinking water?\",\"answer\":\"It forecasts bacteriological presence represented as total cell counts for water exiting drinking water treatment plants.\"},{\"question\":\"How was the model developed and validated?\",\"answer\":\"The model was developed and validated using one year of hourly online flow cytometer data from an operational treatment plant.\"},{\"question\":\"Which forecasting horizon and performance outcomes are reported?\",\"answer\":\"It supports accurate 12-hour-ahead predictions, with the best regression model achieving NSE = 0.96 (K-nearest neighbors) and the best classification model reaching Accuracy = 89.33% using a combined random forest, K-neighbors, and RusBoost approach.\"}]","Forecasting 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