An Innovative Approach to Predict Drinking Water Risks in Michigan Using System, Community, and Regulatory Characteristics

An Innovative Approach to Predict Drinking Water Risks in Michigan Using System, Community, and Regulatory Characteristics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Innovative Approach to Predict Drinking Water Risks in Michigan Using System, Community, and Regulatory Characteristics Liangfei Ye, Qianqian Dong, Aaron McCright, Stephen Gasteyer This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5257706/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Robust predictive models are essential for preventing and mitigating risks associated with public drinking water systems (PWS), which pose significant public health threats and incur substantial medical costs. Methods : This study introduces a novel approach by comparing the performance of Logit, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) models in predicting risks based on PWS characteristics, community attributes, and regulatory developments, rather than relying on water quality and hydrological parameters. Results : The study yields three key findings: (1) XGBoost outperforms Logit and SVM, though all models perform less effectively for predicting health-based risks; (2) community and regulatory characteristics exert a greater influence on risk predictions than PWS characteristics; and (3) XGBoost performs comparably to the water parameter-based prediction approach, with the added benefits of lower cost and suitability for long-term forecasting. Conclusions : This innovative approach offers substantial potential for residents, environmental advocates, and policymakers to better anticipate and address PWS risks by focusing on fundamental social determinants. Machine learning Health Drinking water Risk Prediction Social determinant Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5257706","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":366275197,"identity":"0cf83d8e-b0cc-4f12-bf1c-19a5038eab8d","order_by":0,"name":"Liangfei Ye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYFAC5oYDQJKfH8ojRgsjWIvkzAZStIBIyQ0HiNVicLyx8cDPHbUSxteOP5NgqLBObCCo5czBhoO9Z45LmN3OMZNgOJNOhJYbiQ0HeNuO1QG1sEkwth0mQsv9hw0H/7YdkzCenf5MgvEfMVpuMDYc5m2rkTCQTjCTALIJa5E8k9hwWLbtgITE7Rxji4Rj6cYEtfAdP3z449u2Ogn+2ekPb3yosZYlqEXhAJg6DOElEFIOAvIQQ+uIUTsKRsEoGAUjFQAAEdVHLujqPJ4AAAAASUVORK5CYII=","orcid":"","institution":"Michigan State University","correspondingAuthor":true,"prefix":"","firstName":"Liangfei","middleName":"","lastName":"Ye","suffix":""},{"id":366275198,"identity":"461440a9-c2b4-44a8-aecb-ade3ba1e2e5a","order_by":1,"name":"Qianqian Dong","email":"","orcid":"","institution":"Michigan State University","correspondingAuthor":false,"prefix":"","firstName":"Qianqian","middleName":"","lastName":"Dong","suffix":""},{"id":366275199,"identity":"c437b83f-b167-4e62-beaa-cf8b1841803d","order_by":2,"name":"Aaron McCright","email":"","orcid":"","institution":"Michigan State University","correspondingAuthor":false,"prefix":"","firstName":"Aaron","middleName":"","lastName":"McCright","suffix":""},{"id":366275200,"identity":"14d9b2f3-77ae-4268-b848-1be0e2042d7f","order_by":3,"name":"Stephen Gasteyer","email":"","orcid":"","institution":"Michigan State University","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"","lastName":"Gasteyer","suffix":""}],"badges":[],"createdAt":"2024-10-14 03:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5257706/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5257706/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67715266,"identity":"7f5e9b0a-8e55-45f4-9f51-bda90d72e103","added_by":"auto","created_at":"2024-10-29 03:46:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":494430,"visible":true,"origin":"","legend":"","description":"","filename":"ThePWSriskpredictionpaperSep2024.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5257706/v1_covered_937cd410-5145-4242-874c-bbbc5a34cd85.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Innovative Approach to Predict Drinking Water Risks in Michigan Using System, Community, and Regulatory Characteristics","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Machine learning, Health, Drinking water, Risk, Prediction, Social determinant","lastPublishedDoi":"10.21203/rs.3.rs-5257706/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5257706/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e: \u003c/strong\u003eRobust predictive models are essential for preventing and mitigating risks associated with public drinking water systems (PWS), which pose significant public health threats and incur substantial medical costs.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e: \u003c/strong\u003eThis study introduces a novel approach by comparing the performance of Logit, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) models in predicting risks based on PWS characteristics, community attributes, and regulatory developments, rather than relying on water quality and hydrological parameters.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e: \u003c/strong\u003eThe study yields three key findings: (1) XGBoost outperforms Logit and SVM, though all models perform less effectively for predicting health-based risks; (2) community and regulatory characteristics exert a greater influence on risk predictions than PWS characteristics; and (3) XGBoost performs comparably to the water parameter-based prediction approach, with the added benefits of lower cost and suitability for long-term forecasting.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e: \u003c/strong\u003eThis innovative approach offers substantial potential for residents, environmental advocates, and policymakers to better anticipate and address PWS risks by focusing on fundamental social determinants.\u003c/p\u003e","manuscriptTitle":"An Innovative Approach to Predict Drinking Water Risks in Michigan Using System, Community, and Regulatory Characteristics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-17 02:41:54","doi":"10.21203/rs.3.rs-5257706/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0fe18caf-e157-4608-9023-3cf9e27427be","owner":[],"postedDate":"October 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-29T03:38:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-17 02:41:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5257706","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5257706","identity":"rs-5257706","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}