An improved effective approach for urban air quality forecast

Abstract

Under the circumstance of environment deterioration in cities, people are increasingly concerned about urban environment quality, especially air quality. As a result, it is of great value to provide accurate forecast of air quality index and show the statistics on the smart city platform. In order to forecast urban AQI values the next day, MATLAB is applied to deal with AQI correlative data stored in MySQL database. To be specific, principal component analysis of AQI influencing factors is firstly made, including aspects of weather, industrial waste gas and individual air quality indexes at the present day. Then, to have a better fitting performance than other traditional methods, circular multi-population genetic algorithm (CMPGA) is adopted to optimize initial weights and thresholds of prediction neural network. Afterwards, the improved network is trained to provide AQI forecast the next day. The whole prediction model is named PCA-CMFGA-BP and the core of the model is PCA and CMPGA. To verify accuracy of the model's forecast results, the study uses four statistical indexes to evaluate AQI forecast results (RMSE, MSE, MAPE and MAD) and compares the model with GABP, partial least square regression, principal component estimate regression and support vector regression to prove the model's superiority. To conclude, prediction fitting error is reduced by optimizing parameters of circular multi-population algorithm and choosing the most suitable training function for prediction network. The performance of the model to forecast AQI is comparatively convincing and the model is expected to take positive effect in urban AQI forecast on the smart city platform in the future.