Harmful Algal Bloom Detection System

Harmful Algal Blooms (HABs), typically found in warm water of aquatic ecosystems such as rivers, lakes, and ponds, pose significant threats to ecosystems by affecting livestock, pets, animals, food systems and humans. The economic impact of HABs is estimated at $10-100 million in the United States alone. Traditional methods for detection rely on manual water sampling, sensor data, and satellite imagery. As these methods can be limited by availability, issues with sensors and image resolution, there is a need for effective monitoring of HABs that takes into account their complex dynamics that can lead to widespread ecological disruption. Clemson University researchers has developed a technology that effectively monitors an indicator for HAB occurrences. By coupling AI models such as deep learning tools and data assimilation techniques, this technology can accurately detect HABs and predict concentrations through real-time data processing. This methodology facilitates timely responses to outbreaks, while minimizing costs compared to current detection methods by reducing reliance on continuous sensor data and reducing maintenance and energy needs.