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

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. This study introduces a novel approach by comparing the performance of Logit, Support Vect...

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Published in:Inquiry (00469580) Vol. 63; pp. 1 - 29
Main Authors: Ye, Liangfei, Dong, Qianqian, McCright, Aaron, Gasteyer, Stephen
Format: equations & formulas pictorial research tables/charts Journal Article
Published: Sage Publications Inc. 2/5/2026
Online Access:View this record in EBSCOhost
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      dt: 2/5/2026
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      pub: Sage Publications Inc.
      place: Thousand Oaks, California
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        atl: An Innovative Approach to Predict Drinking Water Risks Using System, Community, and Regulatory Characteristics.
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          Ye, Liangfei
          Dong, Qianqian
          McCright, Aaron
          Gasteyer, Stephen
        affil: Texas A&M University, College Station, TX, USA
      sug:
        subj:
          Water Pollution Prevention and Control
          Water Pollution Risk Factors
          Risk Assessment
          Water Supply Michigan
          Diffusion of Innovation
          Support Vector Machine Utilization
          Boosting Machine Learning Algorithms Utilization
          Government Regulations
          Social Determinants of Health
          Human
          Michigan
          Cross Sectional Studies
          Prediction Models
          Comparative Studies
          Logistic Regression
          Neural Networks (Computer)
          Artificial Intelligence
          Machine Learning
          Descriptive Statistics
          Workforce
          Sociodemographic Factors
      ab: 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. 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. The study yields 3 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. 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.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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