Modelling Phytoplankton Behaviour in the North and Irish Sea with Transformer Networks - Dagtekin, Onatkut; Dethlefs, Nina (Proceedings of the Northern Lights Deep Learning Workshop, 2022)
Climate change will affect how water sources are managed and monitored. Continuous monitoring of water quality is crucial to detect pollution, to ensure that various natural cycles are not disrupted by anthropogenic activities and to assess the effectiveness of beneficial management measures taken under defined protocols. One such disruption is algal blooms in which population of phytoplankton increase rapidly affecting biodiversity in marine environments. The frequency of algal blooms will increase with climate change as it presents favourable conditions for reproduction of phytoplankton. Machine learning has been used for early detection of algal blooms previously, with the focus mostly on single closed bodies of water in Far East Asia with short time ranges. In this work, we study four locations around the North Sea and the Irish Sea with different characteristics predicting activity with longer time-spans and explaining the importance of the input with regard to the output of the prediction model. This work aids domain experts to monitor potential changes to the ecosystem over longer time ranges and to take action when necessary.
Keywords: deep learning; algal bloom; time series; explainability