Location
https://www.kennesaw.edu/ccse/events/computing-showcase/fa24-cday-program.php
Document Type
Event
Start Date
19-11-2024 4:00 PM
Description
E.coli contamination in surface waters has proven to be a significant public health concern, requiring innovative monitoring solutions. This paper presents the design of an AI-driven mobile application to predict whether E.coli bacteria are present at levels exceeding acceptable thresholds in surface waters. The methodology employs sensor devices to collect water quality data parameters, such as water temperature, pH, dissolved oxygen, and turbidity. A dataset is generated based on these parameters, and machine learning (ML) algorithms are applied to evaluate accuracy, precision, recall, and processing time. Additionally, our ML algorithms establish a correlation matrix among water quality parameters to identify the key factors influencing E.coli levels. We applied various machine learning techniques to the dataset, including Support Vector Regression (SVR), Random Forest Classification (RFC), XGBoost, and ensemble methods that combine these algorithms. Our findings indicate that the ensemble of Random Forest Classification and XGBoost achieved the highest accuracy. Users can view E. coli predictions based on current sensor values through our Mobile App.
Included in
GMR-216 AI/ML-Based Water Quality Monitoring Mobile App for Predicting E.coli in Surface Waters
https://www.kennesaw.edu/ccse/events/computing-showcase/fa24-cday-program.php
E.coli contamination in surface waters has proven to be a significant public health concern, requiring innovative monitoring solutions. This paper presents the design of an AI-driven mobile application to predict whether E.coli bacteria are present at levels exceeding acceptable thresholds in surface waters. The methodology employs sensor devices to collect water quality data parameters, such as water temperature, pH, dissolved oxygen, and turbidity. A dataset is generated based on these parameters, and machine learning (ML) algorithms are applied to evaluate accuracy, precision, recall, and processing time. Additionally, our ML algorithms establish a correlation matrix among water quality parameters to identify the key factors influencing E.coli levels. We applied various machine learning techniques to the dataset, including Support Vector Regression (SVR), Random Forest Classification (RFC), XGBoost, and ensemble methods that combine these algorithms. Our findings indicate that the ensemble of Random Forest Classification and XGBoost achieved the highest accuracy. Users can view E. coli predictions based on current sensor values through our Mobile App.