{"id":2067,"date":"2022-05-05T14:14:05","date_gmt":"2022-05-05T14:14:05","guid":{"rendered":"https:\/\/www.ampac1.com\/blog\/developing-a-framework-for-classifying-water-lead-levels-at-private-drinking-water-systems-a-bayesian-belief-network-approach\/"},"modified":"2026-05-03T20:22:10","modified_gmt":"2026-05-04T03:22:10","slug":"developing-a-framework-for-classifying-water-lead-levels-at-private-drinking-water-systems-a-bayesian-belief-network-approach","status":"publish","type":"post","link":"https:\/\/www.ampac1.com\/blog\/developing-a-framework-for-classifying-water-lead-levels-at-private-drinking-water-systems-a-bayesian-belief-network-approach\/","title":{"rendered":"Developing a framework for classifying water lead levels at private drinking water systems: A Bayesian Belief Network approach"},"content":{"rendered":"<header id=\"heading\" class=\"heading\">\\n<\/p>\n<div id=\"full-view-heading\" class=\"full-view\">\\n<\/p>\n<div class=\"inline-authors\">\\n<\/p>\n<div class=\"authors\">\\n<\/p>\n<div class=\"authors-list\"><span class=\"authors-list-item \">Mohammad Ali Khaksar Fasaee<sup class=\"affiliation-links\"><span class=\"author-sup-separator\">\u00a0<\/span><a class=\"affiliation-link\" title=\"Graduate Student, Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA; Graduate Student, Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA. Electronic address: mkhaksa@ncsu.edu.\" href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/33271412\/#affiliation-1\" rel=\"nofollow noopener\" target=\"_blank\">1<\/a><\/sup><span class=\"comma\">,\u00a0<\/span><\/span><span class=\"authors-list-item \">Emily Berglund<sup class=\"affiliation-links\"><span class=\"author-sup-separator\">\u00a0<\/span><a class=\"affiliation-link\" title=\"Professor, Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA. Electronic address: emily_berglund@ncsu.edu.\" href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/33271412\/#affiliation-2\" rel=\"nofollow noopener\" target=\"_blank\">2<\/a><\/sup><span class=\"comma\">,\u00a0<\/span><\/span><span class=\"authors-list-item \">Kelsey J Pieper<sup class=\"affiliation-links\"><span class=\"author-sup-separator\">\u00a0<\/span><a class=\"affiliation-link\" title=\"Assistant Professor, Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA. Electronic address: k.pieper@northeastern.edu.\" href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/33271412\/#affiliation-3\" rel=\"nofollow noopener\" target=\"_blank\">3<\/a><\/sup><span class=\"comma\">,\u00a0<\/span><\/span><span class=\"authors-list-item \">Erin Ling<sup class=\"affiliation-links\"><span class=\"author-sup-separator\">\u00a0<\/span><a class=\"affiliation-link\" title=\"Water Quality Extension Associate, Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA. Electronic address: ejling@vt.edu.\" href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/33271412\/#affiliation-4\" rel=\"nofollow noopener\" target=\"_blank\">4<\/a><\/sup><span class=\"comma\">,\u00a0<\/span><\/span><span class=\"authors-list-item \">Brian Benham<sup class=\"affiliation-links\"><span class=\"author-sup-separator\">\u00a0<\/span><a class=\"affiliation-link\" title=\"Professor, Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA. Electronic address: benham@vt.edu.\" href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/33271412\/#affiliation-5\" rel=\"nofollow noopener\" target=\"_blank\">5<\/a><\/sup><span class=\"comma\">,\u00a0<\/span><\/span><span class=\"authors-list-item \">Marc Edwards<sup class=\"affiliation-links\"><span class=\"author-sup-separator\">\u00a0<\/span><a class=\"affiliation-link\" title=\"Professor, Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA 24061, USA. Electronic address: edwardsm@vt.edu.\" href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/33271412\/#affiliation-6\" rel=\"nofollow noopener\" target=\"_blank\">6<\/a><\/sup><\/span><\/div>\n<p>\\n<\/p><\/div>\n<p>\\n<\/p><\/div>\n<p>\\n<\/p>\n<div class=\"short-article-details\"><\/div>\n<p>\\n<\/p>\n<ul id=\"full-view-identifiers\" class=\"identifiers\">\\n \\t<\/p>\n<li><span class=\"identifier pubmed\"><span class=\"id-label\">PMID:\u00a0<\/span><strong class=\"current-id\" title=\"PubMed ID\">33271412<\/strong><\/span><\/li>\n<p>\\n \\t<\/p>\n<li><span class=\"identifier doi\"><span class=\"id-label\">DOI:\u00a0<\/span>10.1016\/j.watres.2020.116641<\/span><\/li>\n<p>\\n<\/ul>\n<p>\\n<\/p><\/div>\n<p>\\n<\/header>\n<p>\\n<\/p>\n<div id=\"abstract\" class=\"abstract\">\\n<\/p>\n<h2 class=\"title\">Abstract<\/h2>\n<p>\\n<\/p>\n<div id=\"enc-abstract\" class=\"abstract-content selected\">\\n\\nLead in drinking water is a serious public health issue. Even small amounts can cause neurological damage. Our research set out to find better ways to predict lead risk in private water systems. We used something called Bayesian Network approaches to look at how different things interact: household details, geology, what people see in their tap water, and lab test results.\\n\\nWe built a framework for finding knowledge by combining methods for sorting data, picking out key features, and using Bayes classifiers. We tried both &#8220;forward selection&#8221; and &#8220;backward selection&#8221; to pick features. For continuous data, we tested different ways to group it, including using what we already know, statistical methods, and information-based approaches. We also tested a few Bayes classifiers, like General Bayesian Network, Naive Bayes, and Tree-Augmented Naive Bayes, to help us identify how things are connected. Then, we used Bayesian inference to fill in the probability tables for each connection.\\n\\nWe applied this Bayesian system to a dataset from the Virginia Household Water Quality Program (VAHWQP). They collected water samples and surveyed 2,146 households with private water systems, like wells and springs, in Virginia during 2012 and 2013. We looked at how lab-tested water quality, tap water observations, and household features, such as plumbing type, water source, location, and on-site water treatment, all connect to predict lead levels. \\n\\nOur results show that Naive Bayes classifiers work best for recall and precision compared to other classifiers. Copper is the most important predictor of lead. Other big predictors include the county, pH, and whether there&#8217;s on-site water treatment. The way we picked features didn&#8217;t change performance much, but how we grouped the data really affected how well the models worked with the classifiers.\\n\\nOwners of private wells are still at a disadvantage and might face higher risks. Why? Because utilities and government agencies aren&#8217;t responsible for making sure lead levels in private wells meet the Lead and Copper Rule. The insights we gained from these models can help us pinpoint which water quality factors, plumbing types, and household details increase the chance of high lead levels. This information is crucial for making smart decisions about lead testing and treatment.\\n\\n<\/div>\n<p>\\n<strong class=\"sub-title\">Keywords:\u00a0<\/strong>Bayesian Belief Network; Contamination Classification; Lead in Drinking Water; Water Quality.\\n\\n<\/div>\n<p>\\n<\/p>\n<p id=\"copyright\" class=\"copyright\">Copyright \u00a9 2020 Elsevier Ltd. All rights reserved.<\/p>\n<p>\\nThe post Developing a framework for classifying water lead levels at private drinking water systems: A Bayesian Belief Network approach appeared first on Facts About Water.\\n\\nSource: Water Feed\\n<\/p>\n<p>Related reading: <a href=\"https:\/\/www.ampac1.com\/blog\/a-decision-analysis-framework-for-estimating-the-potential-hazards-for-drinking-water-resources-of-chemicals-used-in-hydraulic-fracturing-fluids\/\">A decision analysis framework for estimating the potential hazards for drinking water resources of chemicals used in hydraulic fracturing fluids<\/a>, <a href=\"https:\/\/www.ampac1.com\/blog\/investigations-reveal-startling-scope-of-lead-in-drinking-water\/\">Lead in Drinking Water: Scope, Risks, and Reverse Osmosis Solutions<\/a>, <a href=\"https:\/\/www.ampac1.com\/blog\/avoid-that-lead-lead-free-water-strategies-reverse-osmosis\/\">Avoid That Lead &#8211; Lead Free Water Strategies, Reverse Osmosis<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Mohammad Ali Khaksar Fasaee\u00a01,\u00a0Emily Berglund\u00a02,\u00a0Kelsey J Pieper\u00a03,\u00a0Erin Ling\u00a04,\u00a0Brian Benham\u00a05,\u00a0Marc Edwards\u00a06 PMID:\u00a033271412 DOI:\u00a010.1016\/j.watre&#8230;<\/p>\n","protected":false},"author":1,"featured_media":87919,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center 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