AI-powered Forecast for Harmful Algal Blooms

The AIHABs project, an acronym for Artificial Intelligence for Harmful Algal Blooms, stands as a pioneering initiative aimed at addressing a significant environmental challenge through cutting-edge technologies. This groundbreaking project aims to harness the potential of Artificial Intelligence (AI) to enhance our comprehension and prediction of the occurrence, spread, and fate of Harmful Algal Blooms (HABs) in inland water bodies. Following a comprehensive multi-criteria analysis, two sites with a documented history of HABs (one in Spain and one in the Czech Republic) were identified as optimal locations for the study. HABs refer to a rapid proliferation of toxic algae in a body of water, posing risks to aquatic organisms, human health, and the environment. These blooms can produce harmful substances affecting the well-being of fish, marine mammals, birds, and may also jeopardize human health when water is utilized for drinking or recreational purposes. The AIHABs project integrates cutting-edge innovations in nanosensor technology, remote sensing, and machine learning. Nanosensors play a pivotal role in the AIHABs project, providing real-time, precise data on water quality—crucial for effective HAB detection and management. Remote sensing contributes large-scale, repetitive observations of water bodies, enabling the mapping and monitoring of changes in water quality parameters identified as precursors to HABs. Lastly, machine learning algorithms analyze extensive datasets encompassing water quality, weather conditions, and other pertinent factors. The derived insights are then employed to recognize patterns and predict the likelihood of HAB occurrences. Through the integration of AI, the AIHABs project strives to enhance our capability to detect and respond to HABs, ultimately safeguarding human health, aquatic life, and the environment. By synergizing the power of AI with traditional monitoring methods, the AIHABs project represents a pioneering effort to mitigate the impacts of these harmful events.

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.