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ORIGINAL RESEARCH article
Front. Environ. Eng.
Sec. Water, Waste and Wastewater Engineering
Volume 4 - 2025 | doi: 10.3389/fenve.2025.1488965
This article is part of the Research Topic Artificial Intelligence in Environmental Engineering and Ecology: Towards Smart and Sustainable Cities View all 10 articles
Graph Neural Network-based Water Contamination Detection from Community Housing Information
Provisionally accepted
Raphael Anaadumba 
Yigit Bozkurt 
Connor Sullivan 
Madhavi Pagare 
Pradeep Kurup *
Benyuan Liu 
Mohammad Arif Ul Alam *- University of Massachusetts Lowell, Lowell, United States
Detecting water contamination in community housing is crucial for protecting public health. Early detection enables timely action to prevent waterborne diseases and ensures equitable access to safe drinking water.Traditional methods recommended by the Environmental Protection Agency (EPA) rely on collecting water samples and conducting lab tests, which can be both time-consuming and costly. To address these limitations, this study introduces a Graph Attention Network (GAT) to predict lead contamination in drinking water.The GAT model leverages publicly available municipal records and housing information, to model interactions between homes and identify contamination patterns. Each house is represented as a node, and relationships between nodes are analyzed to provide a clearer understanding of contamination risks within the community.
Keywords: Water contamination, Public Health, Graph Attention Network (GAT), Environmental hazards, Flint Michigan
Received: 31 Aug 2024; Accepted: 27 Feb 2025.
Copyright: © 2025 Anaadumba, Bozkurt, Sullivan, Pagare, Kurup, Liu and Alam. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Pradeep Kurup, University of Massachusetts Lowell, Lowell, United States
Mohammad Arif Ul Alam, University of Massachusetts Lowell, Lowell, United States
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