Abstract:

Total phosphorus (TP) concentration has often been found to do not correlate significantly to other environmental factors,leading to a low prediction accuracy of total phosphorus.In order to improve the prediction accuracy of total phosphorus,we proposed neural network algorithm combined forecasting models  based on  BP,Elman,RBF,GRNN (simplied as BP 4 in the following).Setting the three related factors,i.e.NH+4N,CODMn and transparency  as the input and the measured values as the output,a single forecasting model of 3 input and 1 output was established.Then,using the output of BP 4 single model as the input of next BP 4 model,and total measured values as output,a combination forecasting model with 4 input and 1 output was established; taking this procedure once more and a secondary combination forecasting model with 4 input and 1 output was established,being followed by  construction of 8 schemes of multiple combination forecasting model.The results showed as follows.In the combination models,the prediction accuracy is remarkably increased with increasing weight number of combination,indicating that multiple combination model for lake total phosphorus prediction is reasonable and feasible,and that the model has higher prediction accuracy and generalization ability,and it is the effective method to improve the prediction accuracy.All the predicted results from scheme 2 to scheme 8 were better than that from the GABP model (except for 2 GRNN),indicating that the combination model has high forecasting accuracy and generalization ability.Among them,the average relative error of BP model in scheme 3,plan 4-8 in BP,Elman and RBF model was less than 10%,demonstrating that the prediction accuracy is satisfactory.In the programme from 6 to 8,BP,Elman and RBF model,prediction accuracy is the highest (average relative errors are within 9%),being better than other combination model