Potential Application of Machine Learning Techniques to Identify Prior Limiting Factors as a Basis for Eutrophication Assessment

The aim of this study was to determine the factors that influence eutrophication. The factors causing eutrophication are widely known, but identifying the primary threat for a specific water body remains challenging. The study objects were the warm monomictic urban Dal Lake in Kashmir, India, and the artificial dam reservoir Dobczyce in Poland. Data analysis methods, including multiple regression and artificial neural networks (NNET and NeuralNet) from the R package ver. 4.5.2, were used. Regarding Dal Lake, the factor most influencing the trophic change was total nitrogen. In contrast, for the Dobczyce dam reservoir, water temperature was the dominant factor. Although the regression method did not provide clear results, neural networks enabled the identification of the limiting factors; therefore, the proposed approach may be useful for determining the factors limiting the eutrophication process. The core novelty of this research lies in demonstrating the potential of artificial neural networks to identify key factors causing eutrophication, particularly under conditions of limited data.