Applications of back propagation neural network for predicting the concentration of chlorophyll-a in West Lake

We have established 8 sampling spots in West Lake, and selected spot 7 which can most represent the water quality status of it as study object by principal component analysis. With sufficient samples got by the inserted method, based on the aquatic data (2000.1～2001.4) of West Lake by routine measurement, we studied the feasibility of using Back propagation (BP) neural network to predict the short term trends of the state of aquatic ecology (the concentration of chlorophyll-a), and looked for the most influential elements which can reflect the trends of aquatic ecology in West Lake for modeling. At the same time, we used the data of spot 3 to test the universality of the network, and found the outputs tallied with the measured values very well. The results show that water temperature and chlorophyll-a affect the concentration of chlorophyll-a of next week most greatly. The network using them as input variables is simple and prompt, having greater advantage than other linearity numeric modeling. This indicates that artificial neural network is an effective method for forecasting the concentration of chlorophyll-a. And it can provide the scientific basis for the control of the eutrophication of West Lake.