The accurate prediction of PM2.5 concentration in a agricultural park is important to understand the role agricultural park plays in regulating PM2.5 pollution and guide public close to the nature healthily. An artificial neural network model was established, with meteorological data, atmospheric PM2.5 concentration outside the agricultural park and agricultural park structure as the input factors and PM2.5 hourly average concentration inside the agricultural park as the output factors. Its prediction accuracy was also evaluated in this study. The results show that it can be concluded that BP artificial neural network model is a promising approach in predicting PM2.5 concentration inside a agricultural park.
Results in REF demonstrates the better performance of ANN compared to MLR for prediction of PM2.5 in the agricultural park.
Prediction of PM2.5 Concentration in a Agricultural Park Based on BP Artificial Neural Network
Advance Journal of Food Science and Technology
Environmental Science