Neural network modeling of the eutrophication and strategy of pollution control in Lake Xingyun

Lake Xingyun was selected as a study object. The key factors of the eutrophication were screened out using PCA, and back-propagate neural network was used to simulate the relation between chlorophyll a and key factors, and the pressure-response effect between chlorophyll a and key factors was quantitatively analyzed. The conclusions are: CODMn, TP and TN were the key factors of the eutrophication. Set 0.02 mg/L as the control target of chlorophyll a, then 61% of CODMn or 77% of TP or 20% of TN should be reduced. This result indicated that N was the limiting factor of the phytoplankton in Lake Xingyun. This simulation of eutrophication provided the basic data for the remediation of Lake Xingyun.