Water eutrophication control based on deep learning: analysis of denitrification and phosphorus removal microbial technology and ecological restoration efficiency

This study used deep learning technology to construct a water eutrophication control model, focusing on the mechanism and efficiency evaluation of denitrification and phosphorus removal microorganisms. The complex interplay among microbial community structure, environmental factors, and denitrification efficiency was quantitatively analyzed using an integrated modeling approach that combined a Convolutional Neural Network (CNN) with a Long Short-Term Memory network (LSTM). The study collected 120 groups of eutrophic water samples, applied deep learning algorithms to identify the six key microbial groups that contributed most to the denitrification process, and optimized their growth conditions. The results showed that the microbial treatment technology based on deep learning optimization increased the denitrification efficiency by 32.7% and the nitrogen and phosphorus removal rate by 28.5%. This study provides a new method and theoretical basis for the intelligent monitoring and ecological restoration of water eutrophication, and has important guiding significance for the construction of an intelligent decision-making system for water environment management.