{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T11:42:07Z","timestamp":1784374927052,"version":"3.55.0"},"reference-count":34,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T00:00:00Z","timestamp":1780531200000},"content-version":"vor","delay-in-days":34,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["acsess.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Env Quality"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Corrosion and scaling in drinking water distribution systems negatively affects the pipe lifespan, water quality, and cost of maintenance. Empirical corrosion and scaling indices are derived based on physicochemical water parameters. Each of these indices comes from different chemical assumptions and interpretation ranges. They provide conflicting results for the same sample of water. This study employed a hybrid methodology integrating experimental investigation, statistical analysis, and machine learning modeling toward enhanced evaluation of corrosion. Water samples were collected during an extended monitoring period from a real, running drinking water distribution system and analysed for their physicochemical properties. Corrosion rate determination was performed experimentally by the weight\u2010loss method; an entropy\u2010weighted water quality index was also calculated. The results were validated against the experimentally taken corrosion rate. A gradient boosting machine learning (extreme gradient boosting) model was used. It predicted the corrosion behavior with high accuracy. To resolve the inconsistency problem between individual indices, a unified corrosion\u2013scaling index (UCSI) is developed by normalising all of them to a common scale for corrosion tendency and then averaging their values into a single, easily interpretable metric. This index provides each water sample a clear corrosive, neutral, or scale\u2010forming classification. UCSI exhibited stable and consistent classification across both training datasets (neutral or borderline: 42.12%\u201350.76%, scale\u2010forming: 25.76%\u201336.36%, and corrosive: 21.52%\u201323.48%) and testing datasets (neutral or borderline: 44.52%\u201345.45%, scale\u2010forming: 39.39%\u201339.42%, and corrosive: 15.16%\u201316.06%). These results show close agreement between the observed and model\u2010predicted results.<\/jats:p>","DOI":"10.1002\/jeq2.70201","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:35:57Z","timestamp":1780637757000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integrated assessment of corrosion behaviour in drinking water distribution systems using experimental analysis, machine learning, and a unified corrosion\u2013scaling index"],"prefix":"10.1002","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3017-3220","authenticated-orcid":false,"given":"Saurabh","family":"Kumar","sequence":"first","affiliation":[{"name":"Research Unit in Data Science and Digital Transformation, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering Thammasat University  Pathumthani Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9109-4270","authenticated-orcid":false,"given":"Divesh Ranjan","family":"Kumar","sequence":"additional","affiliation":[{"name":"Research Unit in Data Science and Digital Transformation, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering Thammasat University  Pathumthani Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reena","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering National Institute of Technology Patna  Patna India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nityanand Singh","family":"Maurya","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering National Institute of Technology Patna  Patna India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1006-6540","authenticated-orcid":false,"given":"Warit","family":"Wipulanusat","sequence":"additional","affiliation":[{"name":"Research Unit in Data Science and Digital Transformation, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering Thammasat University  Pathumthani Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,4]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/ECWS\u20107\u201014316"},{"key":"e_1_2_10_3_1","volume-title":"Standard methods for the examination of water and wastewater","author":"American Public Health Association","year":"2017"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.17344\/acsi.2022.7939"},{"key":"e_1_2_10_5_1","volume-title":"Standard practice for preparing, cleaning, and evaluating corrosion test specimens","author":"ASTM","year":"1990"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.12912\/27197050\/175970"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.watres.2024.120845"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jwpe.2025.107975"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41545\u2010023\u201000275\u20105"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.grets.2025.100254"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11356\u2010024\u201035691\u20108"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10653\u2010025\u201002495\u20109"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1080\/24705314.2025.2471682"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10532\u2010025\u201010152\u20102"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2021.02.503"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1680\/JENES.21.00022"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.46488\/NEPT.2022b.v21i05.008"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11356\u2010023\u201025465\u2010z"},{"key":"e_1_2_10_19_1","doi-asserted-by":"crossref","unstructured":"Kumar S. 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