Prediction of Shanghai air quality index based on BP neural network optimized by genetic algorithm

Ruijun Yang, Xueqi Hu, Lijun He

Abstract: This paper uses PCA (principal component analysis) combined with bp neural network and neural network based on genetic algorithm optimization to predict Shanghai's AQI (air quality index) respectively. Matlab is used for modeling and simulation. which the prediction and analysis are different The error value and the number of iterations under the algorithm. The results show that the neural network optimized by genetic algorithm can effectively reduce the prediction error of the air quality index compared with the combination of PCA and bp neural network, making the optimized neural network prediction accuracy rate of 90.7%, greatly improving the neural network The learning efficiency has a good performance in predicting the air quality in Shanghai.