newswise.com
BloomNet turns lake data into early algal bloom warnings
Newswise — Harmful algal blooms are becoming more frequent and widespread as warming, eutrophication, agricultural runoff, aquaculture, and wastewater alter freshwater ecosystems. Once a bloom is visible, treatment can be costly, ineffective, or environmentally disruptive, making early warning essential. Yet many forecasting systems work at daily or weekly resolution, rely on fixed thresholds, or return only one predicted value without showing uncertainty. Deep-learning models may improve accuracy, but their decision processes often remain difficult to interpret, especially when drivers change rapidly before bloom onset. These gaps point to a need for in-depth research to develop high-frequency forecasting systems that quantify risk and explain when and why harmful algal blooms may emerge. The study was conducted by researchers from Beijing Institute of Technology, China University of Mining and Technology, and the Research Center for Eco-Environmental Sciences of the Chinese Academy of Sciences. Published (DOI: 10.1016/j.ese.2026.100733) online on July 21, 2026, in Environmental Science and Ecotechnology, the work introduces BloomNet as a multi-horizon forecasting system for harmful algal blooms. The model combines historical water-quality observations with environmental information available over the forecast window, producing hourly algal-density predictions for the next 24, 48, and 72 hours together with interpretable indicators of uncertainty and...