Predicting Model of Algal Blooms Based on BP Neural Network

According to the 30 weeks of monitored data from March to October in 2009 in the School pond of Ningbo University,we constructed a predicting model to deal with the relation between the density of Oscillatoria and 6 environmental factors such as total nitrogen,total phosphorus,secchi depth,etc with the back propagation artifical neutral network method.We selected the best predicting model,and sensitivity analysis was performed to the model.The results showed that the forecasted value of the density of Oscillatoria according to the BP neural network predicting model had a better fit with actual value of the density of Oscillatoria,and the correlation coefficient achieved 0.984,it indicated the BP neural network predicting model can be used for short-term forecast of the algal blooms;and through carried on sensitivity analysis to the constructed BP neural network predicting model,it clarified the main driver factor of algal blooms in the School pond of Ningbo University,and the result showed that controlling PH value would be important to prevent and control the algal blooms in the School pond of Ningbo University.