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AI-powered Forecast for Harmful Algal Blooms


Eutrophication of water bodies and global warming in Europe are contributing to the increment of cyanobacteria to excessive levels, causing Harmful Algal Blooms (HABs). Cyanobacteria are very well-known producers of secondary metabolites that very frequently are toxic to humans and other organisms. Those metabolites, called in general cyanotoxins, are one of the issues of maximum concern in reference to water quality and effects on population. They appear very frequently in water bodies in Europe, are highly toxic, harming human health and the aquatic ecosystems, and many of them are almost unknown in terms of producers and ecological dynamics. Thus, monitoring and early detection of HABs is essential for the establishment of effective water governance policies. AIHABs is a multidisciplinary innovative initiative aimed at developing an integrated evaluation system to forecast the risk derived from the presence of emerging cyanotoxins in inland and coastal ecosystems. The innovation of this proposal resides at merging tools as last generation Artificial Intelligence (AI), remote sensing, nanosensors, hydrodynamic modelling and massive genetic sequencing with the joint purpose of providing an early warning system to decision making authorities in terms of risk to the population. The predicting modelling effort will allow a timely action to minimize the risks of consuming surface waters or using them as recreational resources when the waterbodies are prone to produce toxic cyanobacterial blooms.

Project leader: Marcos Xose Alvarez Cid

Started: 2021

Ends: 2024

Category: Universiteter

Sector: UoH-sektor

Budget: 1279999


Address: NA