基于BP神经网络的水体富营养化评价的研究

Study on Evaluation of Eutrophication Based on BP Neural Network

水体富营养化改变了水体的理化性质,不仅破坏生态的平衡还严重影响人类的健康.因此,对水体富营养化的研究是一件有意义的研究工作.BP神经网络由于操作简便易行,可以自组织、自学习、自适应并具有容错和抗干扰能力等特点,已成为水体富营养化评价的一个热门.文章应用BP神经网络对泉州市山美水库的水体营养状况进行评价,并与综合营养指数法进行了比较,提出了富营养化的防治对策.

Eutrophication of water changes the physical and chemical nature of water, which not only damages the ecological balance but also impacts on human health seriously. So the study of eutrophication has become a significant research. This paper uses BP neural netwark to evaluate eutrophication, because the process of operation is user-friendly and can be self-organization, self-learning, adaptive, what＇s more,it has anti-jamming capabilities,and fault-tolerant features. It has become a popular evaluation of eutrophication. Using BP neural netwark to evaluate the degree of water eutrophication in shanmei Reservoir,and comparing with TLI method, the paper propses the prevention countermeasures for eutrophi cation of water.