BloomSense: Integrating Automated Buoy Systems and AI to Monitor and Predict Harmful Algal Blooms

Algal blooms pose significant risks to public health and aquatic ecosystems, highlighting the need for real-time water quality monitoring. Traditional manual methods are often limited by delays in data collection, which can hinder timely response and effective management. This study proposes a solution by integrating automated monitoring systems (AMSs) with advanced machine learning (ML) techniques to predict chlorophyll-a (Chl−a) concentrations. Utilizing low-cost and readily available input variables, we developed energy-efficient ML algorithms optimized for deployment on buoys with a battery and hardware resources. The AMS employs preprocessing methods like the SMOTE and Random Forest (RF) for feature selection and ranking. Deep feature extraction is performed through a ResNet-18 model, while temporal dependencies are captured using a Long Short-Term Memory (LSTM) network. A Softmax output layer then predicts Chl−a concentrations. An alert system is incorporated to warn when Chl−a levels exceed 10 μg/L, signaling potential bloom conditions. The results show that this approach offers a rapid, cost-effective, and scalable solution for real-time water quality monitoring, enhancing manual sampling efforts and improving management of water bodies at risk.