Artificial Intelligence-powered Forecast for Harmful Algal Blooms


Project coordinator: Dr Ahmed NASR -

Technological University Dublin (TU Dublin) - Ireland

Communication contact:   - Marcos Xosé ÁLVAREZ CID - marcos.alvarez(at)

Dr Marcos Xosé Álvarez Cid Norwegian University of Science and Technology (NTNU) Norway
Dr Mohammadmehdi Saberioon Helmholtz Centre Potsdam - German Research Centre for Geosciences (GFZ) Germany
Dr Jakub Brom The University of South Bohemia in České Budějovice (USB) Czech Republic
Dr Begoña Espiña International Iberian Nanotechnology Laboratory (INL) Portugal
Prof. Antonio Quesada Universidad Autónoma de Madrid (UAM) Spain
Prof. Fernando Cobo Gradín University of Santiago de Compostela (USC) Spain


Eutrophication of water bodies in Europe is contributing to the increase of Harmful Algal Blooms (HABs) which poses a 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 water bodies 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.

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TU Dublin


TU Dublin


Expected research results

  • AI-based software for forecasting water quality in inland and coastal water bodies
  • Calibration, training and validation methodologies to correlate the results of the microbiological analysis in the lab and the output of the AI-based predictive model
  • Computer vision algorithms based on deep learning for detecting algae in water bodies
  • Validation of the effectiveness and efficiency of the monitoring and forecasting subsystems of the AIHABs in a coastal water body and in a reservoir

For more details on the work plan and expected impact of the project consult the AquaticPollutants booklet.

Keywords: Water quality, Harmful Algal Blooms (HABs), Hydrodynamics, Remote sensing, Computer vision, Artificial Intelligence (AI)

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published on 2021/10/15 10:29:00 GMT+1 last modified 2022-07-08T11:16:13+01:00