Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
Document Type
Event
Start Date
22-4-2026 4:00 PM
Description
Water quality monitoring is essential for public health and environmental sustainability, yet existing monitoring infrastructures remain sparse, fragmented, and incomplete. Data from the United States Geological Survey (USGS) indicate that while over 1.5 million sites are cataloged in the USGS Water Data for the Nation, only a small fraction are actively reporting water quality measurements, with significant reductions observed in recent years. Moreover, critical parameters such as pH, water temperature, dissolved oxygen, turbidity, and microbial indicators like Escherichia coli are inconsistently measured, with widespread missing and irregular data. This work presents an AI-enabled water quality data framework designed to address these limitations by integrating heterogeneous environmental datasets, reconstructing missing observations, and enabling predictive analytics. The proposed framework incorporates spatio-temporal modeling and location-aware representations to capture upstream–downstream dependencies and environmental influences such as rainfall and watershed dynamics. Building on this foundation, we develop predictive models for E. coli concentration and contamination risk forecasting, transforming sparse and incomplete monitoring data into actionable insights. Building on this foundation, we transform sparse monitoring data into actionable insights through continuous forecasting and binary risk classification. Our proposed regression model captures the underlying environmental physics, explaining 71% of the variance (R2=0.71) in same-day E. coli loads. While continuous 24-hour advanced forecasting is limited by irregular testing data (R2=0.40), our novelty lies in deploying a binary safety classifier that successfully forecasts actionable public health hazards one day in advance with an overall accuracy of 74%.
Included in
GRM-010-170 AI-Enabled Water Quality Framework for E. coli Prediction and Forecasting
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
Water quality monitoring is essential for public health and environmental sustainability, yet existing monitoring infrastructures remain sparse, fragmented, and incomplete. Data from the United States Geological Survey (USGS) indicate that while over 1.5 million sites are cataloged in the USGS Water Data for the Nation, only a small fraction are actively reporting water quality measurements, with significant reductions observed in recent years. Moreover, critical parameters such as pH, water temperature, dissolved oxygen, turbidity, and microbial indicators like Escherichia coli are inconsistently measured, with widespread missing and irregular data. This work presents an AI-enabled water quality data framework designed to address these limitations by integrating heterogeneous environmental datasets, reconstructing missing observations, and enabling predictive analytics. The proposed framework incorporates spatio-temporal modeling and location-aware representations to capture upstream–downstream dependencies and environmental influences such as rainfall and watershed dynamics. Building on this foundation, we develop predictive models for E. coli concentration and contamination risk forecasting, transforming sparse and incomplete monitoring data into actionable insights. Building on this foundation, we transform sparse monitoring data into actionable insights through continuous forecasting and binary risk classification. Our proposed regression model captures the underlying environmental physics, explaining 71% of the variance (R2=0.71) in same-day E. coli loads. While continuous 24-hour advanced forecasting is limited by irregular testing data (R2=0.40), our novelty lies in deploying a binary safety classifier that successfully forecasts actionable public health hazards one day in advance with an overall accuracy of 74%.