Artificial Intelligence-powered Forecast for Harmful Algal Blooms
The eutrophication of water bodies in Europe is contributing to the increase of Harmful Algal Blooms (HABs) which poses serious risk to human health. To address this problem, the AIHABs project will develop an early warning system to forecast the occurrence, spread and fate of cyanotoxins caused by HABs in inland and coastal waters, using Artificial Intelligence (AI) and the latest innovations in mathematical modelling, nanosensors, and remote sensing. The novelty of this project lies in merging these tools with the joint purpose of providing an early warning system to decision-making authorities in terms of risk to the public. The model predictions will allow timely action to minimise the risks of consuming surface waters or using them as recreational resources when the waterbodies are prone to produce toxic cyanobacterial blooms.
A number of candidate sites with a history of HABs in the countries of the project partners will be evaluated using multi-criteria analysis in order to identify the most suitable inland and coastal water sites for use in the study. The main criteria for selecting the sites will be the availability of the required data for modelling and the strong evidence of historical HABs.
Water quality, Harful Algal Blooms (HABs), Hydrodynamics, Remote sensing, Computer vision, Artificial Intelligence (AI)
Dr. Ahmed Nasr,
Technological University Dublin (TU Dublin), Ireland
Communication & Dissemination Contact:
Marcos Xosé Álvarez Cid
Norwegian University of Science and Technology (NTNU) – Norway
Helmholtz Centre Potsdam, German Research Centre for Geosciences (GFZ) – Germany
University of South Bohemia in České Budějovice (USB) – Czech Republic
International Iberian Nanotechnology Laboratory (INL) – Portual
Universidad Autónoma de Madrid (UAM) – Spain
University of Santiago de Compostela (USC) – Spain