Air Quality Prediction Using Improved PSO-BP Neural Network

Abstract

Predicting urban air quality is a significant aspect of preventing urban air pollution and improving the living environment of urban residents. The air quality index (AQI) is a dimensionless tool for quantitatively describing air quality. In this paper, a method for optimizing back propagation (BP) neural network based on an improved particle swarm optimization (PSO) algorithm is proposed to predict AQI. The improved PSO algorithm optimizes the variation strategy of the inertia weight as well as the learning factor, guaranteeing its global search ability during the early stage and later enabling its fast convergence to the optimal solution. We introduce an adaptive mutation algorithm during the search process to avoid the particles from falling into the local optimum. Through an analysis and comparison of the experimental results, BP neural network optimized using the improved PSO algorithm achieves a more accurate prediction of AQI.