Eileen Weiyun Ho¹ and Armando Contreras², ¹USA, ²California State Polytechnic University, USA

Indicator organisms such as Escherichia coli (E. coli) are vital for monitoring microbiological water quality [16]. However, current testing methods are reactive, which may cause delays in reporting E. coli levels after contamination. This can make timely interventions difficult, especially in locations lacking in testing infrastructure. Our proposal involves the creation of a machine learning-based algorithm and application that predicts and displays microbiological water quality and any potential infractions. Our research examined correlations between E. coli levels, date, and temperature. We found that E. coli levels peaked in July, modeled by an exponential trendline; temperature showed a strong correlation, likely due to its influence in the other variables. We also validated our app's predictions of E. coli levels using data from the Massachusetts Department of Public Health (MDPH) data. Our application had an average prediction difference of 2 units across 50 locations. These findings suggest reliable, real-time water safety information. Through machine learning, our application aims to provide proactive insights into water quality to enhance public health and safety.

Pathogenic Bacteria Prediction, Water Quality Prediction, Machine Learning, Environmental Health and Safety