New PM2.5 forecasting system based on combined neural network and an improved multi-objective optimization algorithm: Taking the economic belt surrounding the Bohai Sea as an example

Abstract

PM2.5 forecasting has always been difficult. Neural networks are widely used in the forecasting of PM2.5, however, the accuracy is not satisfactory due to uncertainty. In order to solve this problem, a new PM2.5 forecasting system based on data preprocessing, combined neural network, and the unconstrained weighting method based on an improved multi-objective optimization algorithm is proposed. A new chaotic map is introduced to our model to improve the traditional population initialization. In addition, a new unconstrained weighting method was proposed, and a combination of MAPE value (prediction accuracy) and STD value (prediction stability) were set as the objective functions. The predicted results of each neural network were judged by statistical index and a dynamic weight was given to different neural networks to achieve the optimal prediction result. We demonstrated the superiority, accuracy and generalization ability of our proposed model through experiments on the PM2.5 data of four cities around the Bohai Economic Belt in China: Beijing, Tianjin, Dalian, and Yantai.