Prediction of environmental factors responsible for chlorophyll a-induced hypereutrophy using explainable machine learning

Hypereutrophy of water bodies is an undesirable phenomenon due to the influence of a complex of environ-mental factors. The aim of the work was to predict the responsibility of environmental factors in changing water quality from eutrophic to hypereutrophic states. Hypereutrophy was defined as exceeding the concentration of chlorophyll a of 56 mu g/L according to the Trophic State Index (TSI). The study was conducted on the Vistula Lagoon in the southern Baltic Sea. The work was carried out using the prediction and explanation ensemble XGBoost with SHAP modelling. On the global scale of the whole basin and at local sites, the importance of a number of physicochemical, nutritional and phytoplankton factors for hypereutrophy was calculated using mean Shapley values. High concentrations of organic carbon and nitrogen forms and, to a lesser extent, high water temperature mainly caused hypereutrophy. In the case of cyanobacterial biomass, the strong effect of low values of this factor maintains the eutrophic state more stable than higher values maintain hypereutrophy. XGBoost-SHAP modelling as an explanatory tool for the interpretation of water monitoring results can be useful for better management of coastal and inland waters.