PM_2.5 Air Quality Prediction Based on Image Quality Analysis

To improve the prediction accuracy of air pollutants of the mass concentration of PM_2.5, a method of PM_2.5 mass concentration prediction based on collected image data were proposed. First, image data were acquired by mobile phones or cameras, and then feature vectors related to PM_2.5 mass concentration were extracted by image quality analysis model as input. A support vector regression (SVR) prediction model based on particle swarm optimization (PSO) algorithm (PSO-SVR) was established to estimate the mass concentration of PM_2.5. Results show that the prediction accuracy and efficiency of the PSO-SVR model are better than that of the SVR model and the support vector regression model optimized by genetic algorithm (GA-SVR).