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.
展开▼
