Artificial intelligence that enhances remote monitoring of water bodies – highlighting quality shifts due to climate change or pollution – has been developed by researchers at the University of Stirling.

A new algorithm – known as the ‘meta-learning’ method – analyses data directly from satellite sensors, making it easier for coastal zone, environmental and industry managers to monitor issues such as harmful algal blooms (HABs) and possible toxicity in shellfish and finfish.

Environmental protection agencies and industry bodies currently monitor the ‘trophic state’ of water – its biological productivity – as an indicator of ecosystem health. Large clusters of microscopic algae, or phytoplankton, is called eutrophication and can turn into HABs, an indicator of pollution and which pose risk to human and animal health.

HABs are estimated to cost the Scottish shellfish industry £1.4 million per year, and a single HAB event in Norway killed eight million salmon in 2019, with a direct value of over £74 million.

Challenge

Lead author Mortimer Werther, a PhD Researcher in Biological and Environmental Sciences in Stirling’s Faculty of Natural Sciences, said: “Currently, satellite-mounted sensors, such as the Ocean and Land Instrument (OLCI), measure phytoplankton concentrations using an optical pigment called chlorophyll-a. However, retrieving chlorophyll-a across the diverse nature of global waters is methodologically challenging.

“We have developed a method that bypasses the chlorophyll-a retrieval and enables us to estimate water health status directly from the signal measured at the remote sensor.”

Original Article: Stirling experts develop artificial intelligence to monitor water quality more effectively

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