Application of BP neural network optimized by genetic simulated annealing algorithm to prediction of air quality index in Lanzhou

Abstract

It is of great significance to carry out cities' air quality forecasting work for the prevention of the air pollution in urban areas and to the improvement of the living environment of urban residents. The air quality index (AQI) is a dimensionless index that quantitatively describes the state of air quality. In this paper, the data of air quality in Lanzhou released by china air quality online monitoring and analysis platform is dealt with, and then AQI prediction model based on back propagation (BP) neural network, AQI prediction model based on genetic algorithm optimization and AQI prediction model of BP neural network based on genetic simulated annealing algorithm optimization are established. By comparing and analyzing the prediction results, it is found that BP neural network based on genetic simulated annealing algorithm has strong generalization ability and global search ability, and has higher accuracy rate.