Air visibility forecast based on genetic neural network model

Due to the poor air visibility caused by smog, it brings a lot of inconvenience to people's commute. Based on this phenomenon, an air visibility forecasting model was proposed, which is based on the genetic neural network algorithm. In the air visibility forecasting model, the data handled by the principal components analysis (PCA) over seven meteorological factors and six pollutant concentrations factors, which were related with the air visibility, were used as the input, and the air visibility at time 8: 00 and 14: 00 were used as the output data. The model can overcome the problems of flat area and local optimal solutions in BP neural network model. In this paper, the genetic neural network model was trained with the data from Jan. 1 to Aug. 16, 2013 in the city of Xi'an. The visibility of Aug. 17 to Aug. 23 could be estimated by the trained model with the input data got from grey model. Compared with the BP neural network model, the results show that the genetic neural network based air visibility predicting model performs better than the BP neural network model in terms of correlation coefficient and absolute error, thus, it can provide much more accurate forecast for the air visibility.