{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:54:00Z","timestamp":1781196840491,"version":"3.54.1"},"reference-count":39,"publisher":"IWA Publishing","issue":"4","license":[{"start":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T00:00:00Z","timestamp":1641772800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["iwaponline.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In the fast-changing world with increased water demand, water pollution, environmental problems, and related data, information on water quality and suitability for any purpose should be prompt and reliable. Traditional approaches often fail in the attempt to predict water quality classes and new ones are needed to handle a large amount or missing data to predict water quality in real time. One of such approaches is machine-learning (ML) based prediction. This paper presents the results of the application of the Na\u00efve Bayes, a widely used ML method, in creating the prediction model. The proposed model is based on nine water quality parameters: temperature, pH value, electrical conductivity, oxygen saturation, biological oxygen demand, suspended solids, nitrogen oxides, orthophosphates, and ammonium. It is created in Netica software and tested and verified using data covering the period 2013\u20132019 from five locations in Vojvodina Province, Serbia. Forty-eight samples were used to train the model. Once trained, the Na\u00efve Bayes model correctly predicted the class of water sample in 64 out of 68 cases, including cases with missing data. This recommends it as a trustful tool in the transition from traditional to digital water management.<\/jats:p>","DOI":"10.2166\/wst.2022.006","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T18:15:16Z","timestamp":1641838516000},"page":"1027-1039","update-policy":"https:\/\/doi.org\/10.2166\/iwapcrossmarkpolicypage","source":"Crossref","is-referenced-by-count":38,"title":["Water quality prediction based on Na\u00efve Bayes algorithm"],"prefix":"10.2166","volume":"85","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7055-3845","authenticated-orcid":false,"given":"M.","family":"Ili\u0107","sequence":"first","affiliation":[{"name":"Department of Water Management, Faculty of Agriculture, University of Novi Sad, Trg Dositeja Obradovi\u0107a 8, 21 000, Novi Sad, Republic of Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Z.","family":"Srdjevi\u0107","sequence":"additional","affiliation":[{"name":"Department of Water Management, Faculty of Agriculture, University of Novi Sad, Trg Dositeja Obradovi\u0107a 8, 21 000, Novi Sad, Republic of Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B.","family":"Srdjevi\u0107","sequence":"additional","affiliation":[{"name":"Department of Water Management, Faculty of Agriculture, University of Novi Sad, Trg Dositeja Obradovi\u0107a 8, 21 000, Novi Sad, Republic of Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"945","published-online":{"date-parts":[[2022,1,10]]},"reference":[{"key":"2022030413164881900_WST-EM211210C1","article-title":"Pollution migration and transformation along lateral canals in Vojvodina","year":"2005"},{"key":"2022030413164881900_WST-EM211210C3","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.envsoft.2012.03.012","article-title":"Good practice in Bayesian network modelling","volume":"37","year":"2012","journal-title":"Environmental Modelling & 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