[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126541-en":3,"doc-seo-126541-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126541,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Predicting iron exceedance risk in drinking water distribution systems using machine learning","A machine learning framework predicts iron threshold exceedances in sub-regions of drinking water distribution networks using data from the previous year. Models are trained with parameters derived from Self-Organising Map analysis applied to ten years of water quality sampling, pipe information, and customer discolouration contacts from a UK network serving over 2.3 million households. Twenty input parameter sets are evaluated across three algorithms, and the best Random Forest model achieves over 70% accuracy for the UK regulatory 200 µg/L level. Predicted probabilities support relative risk ranking for proactive management.","This is a repository copy of Predicting iron exceedance risk in drinking water distribution systems using machine learning.  \nWhite Rose Research Online URL for this paper:  \n[https://eprints.whiterose.ac.uk/195577/](https://eprints.whiterose.ac.uk/195577/)  \nVersion: Published Version  \nProceedings Paper:  \nKazemi, E., Kyritsakas, G., Husband, [S. orcid.org/0000-0002-2771-1166](S. orcid.org/0000-0002-2771-1166) et al. (3 more authors) (2023) Predicting iron exceedance risk in drinking water distribution systems using machine learning. In: IOP Conference Series: Earth and Environmental Science. 14th International Conference on Hydroinformatics, 04-08 Jul 2022, Bucharest, Romania. IOP Publishing , 012047.  \n[https://doi.org/10.1088/1755-1315/1136/1/012047](https://doi.org/10.1088/1755-1315/1136/1/012047)  \nReuse  \nThis article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)  \nTakedown  \nIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by  \nemailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request.  \n[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk/](https://eprints.whiterose.ac.uk/)  \nPAPER • OPEN ACCESS  \nPredicting iron exceedance risk in drinking water distribution systems using machine learning  \nTo cite this article: Ehsan Kazemi et al 2023 IOP Conf. Ser.: Earth Environ. Sci. 1136 012047  \nView the article online for updates and enhancements.  \nYou may also like  \n-Clustering indices and decay of correlations in non-Markovian models  \nMiguel Abadi, Ana Cristina Moreira Freitas and Jorge Milhazes Freitas  \n-A seven-fold rise in the probability of exceeding the observed hottest summer in India in a 2 °C warmer world  \nNanditha J S, Karin van der Wiel, Udit Bhatia et al.  \n-Population co-exposure to extreme heat and wildfire smoke pollution in California during 2020  \nNoam Rosenthal, Tarik Benmarhnia, Ravan Ahmadov et al.  \nThis content was downloaded from IP address [176.252.227.124](176.252.227.124) on 27/01/2023 at 09:36  \nIOP Conf. Series: Earth and Environmental Science 1136 (2023) 012047 doi:10.1088/1755-1315/1136/1/012047  \nPredicting iron exceedance risk in drinking water distribution systems using machine learning  \nEhsan Kazemi 1, Grigorios Kyritsakas 1, Stewart Husband 1, Katrina Flavell2, Vanessa Speight 1 and Joby Boxall 1  \n1 Department of Civil and Structural Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK  \n2 Yorkshire Water Services Limited, Western House, Halifax Road, Bradford BD6 2SZ, UK  \n[e.kazemi@sheffield.ac.uk](e.kazemi@sheffield.ac.uk)  \nAbstract. A Machine Learning approach has been developed to predict iron threshold exceedances in sub-regions of a drinking water distribution network from data collected the previous year. Models were trained using parameters informed by Self-Organising Map analysis based on ten years of water quality sampling data, pipe data and discolouration customer contacts from a UK network supplying over 2.3 million households. Twenty combinations of input parameters (network conditions) and three learning algorithms (Random Forests, Support Vector Machines and RUSBoost Trees) were tested. The best performing model was found to be Random Forests with input parameters of iron, turbidity, 3-day Heterotrophic Plate Counts, and high priority dead ends per District Metered Area. Different exceedance levels were tested and prediction accuracies of above 70% were achieved for UK regulatory concentration of 200 µg/L. Predicted probabilities per networ","cbCaikvdQdtQFhLw","https://ap.wps.com/l/cbCaikvdQdtQFhLw","pdf",1458547,3,1,11,"English","en",105,"# Abstract\n# Introduction\n## Background and problem motivation\n## Data characteristics and limitations\n# Method overview\n## Feature engineering with Self-Organising Maps\n## Learning algorithms and model comparison\n# Results and risk ranking\n## Exceedance thresholds and accuracy\n## Proactive decision support","[{\"question\":\"What does the machine learning approach predict in the drinking water network?\",\"answer\":\"It predicts iron threshold exceedances in sub-regions of a drinking water distribution network based on prior-year data, enabling early identification of where regulatory exceedances may occur.\"},{\"question\":\"How are the model inputs and parameters constructed?\",\"answer\":\"Inputs combine network-condition parameters, pipe-related information, and discolouration customer contacts, with parameters informed by Self-Organising Map analysis of ten years of water quality sampling data.\"},{\"question\":\"Which algorithm performed best and what accuracy was achieved?\",\"answer\":\"The best model used Random Forests, achieving over 70% prediction accuracy for the UK regulatory concentration threshold of 200 µg/L.\"}]","Predicting iron exceedance risk in drinking water distribution systems using machine learning | 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does the machine learning approach predict in the drinking water network?","Question",{"text":76,"@type":77},"It predicts iron threshold exceedances in sub-regions of a drinking water distribution network based on prior-year data, enabling early identification of where regulatory exceedances may occur.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are the model inputs and parameters constructed?",{"text":81,"@type":77},"Inputs combine network-condition parameters, pipe-related information, and discolouration customer contacts, with parameters informed by Self-Organising Map analysis of ten years of water quality sampling data.",{"name":83,"@type":74,"acceptedAnswer":84},"Which algorithm performed best and what accuracy was achieved?",{"text":85,"@type":77},"The best model used Random Forests, achieving over 70% prediction accuracy for the UK regulatory concentration threshold of 200 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