I-Corps: Translation Potential of Artificial Intelligence-Enabled Bacterial Forecasting System for Water Quality Monitoring

Kennesaw State University Research And Service Foundation, Kennesaw GA

Investigators

Abstract

The broader impact of this I-Corps project is the development of bacterial level monitoring and forecasting systems for measuring water quality in real time. Currently, accurate water quality data is not widely available in real time for recreational waters used for swimming, boating, and fishing. This lack of data can impact public health and aquatic ecosystems. This bacterial monitoring and forecasting system integrates artificial intelligence (AI)-enabled, real-time monitoring and predictive analytics. The system may offer an early warning of bacterial contamination and may enable management by municipal and industrial water agencies, and environmental and private sector entities. The system design may provide an attractive option for widespread adoption in both developed and developing regions, offering a scalable solution that meets the growing demand for water quality management tools. In addition, the technology may provide affordable and effective water quality monitoring solutions in underserved and resource-limited communities. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of an artificial intelligence (AI)-enabled bacterial forecasting system for real-time water quality monitoring. The technology combines long range wide area network (LoRaWAN)-based sensors with an AI-driven cloud platform to collect and analyze water quality data remotely. Leveraging AI algorithms, the system optimizes network parameters to ensure reliable data transmission, energy efficiency, and extended communication range. These technical innovations enable the system to deliver real-time alerts and predictive analytics through a mobile app, providing users with timely information on water safety, particularly for early E. coli detection. The system’s data may offer valuable insights into water quality and bacterial contamination and has the potential to improve outcomes in public health, water resource management, and urban planning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.