Air Quality Index Forecasting Based on SVM and Moments

A novel air quality index (AQI) forecasting method based on support vector machine (SVM) and moments is proposed in this paper. By using it, the AQI value of a sky image can be forecasted. In order to improve the forecasting accuracy and stability, color moments, color correlogram and wavelet features are used to extract the image features, which transfers the input image from color space to feature space. We train the SVM with the input of these extracted features. Then the trained SVM is utilized to achieve the AQI forecasting for new images. The experimental results improve that the proposed method has good AQI forecasting results, and the accuracy of the testing dataset is more than 82.3%, with 1300 training images and 200 test images.