Learn to Predict PM2.5 Concentration with Image Contrast-Sensitive Features

PM2.5, which severely affects human health, is one of the most important indices for air quality estimation. There have been limited studies on the simple, fast and cheap PM2.5 concentration prediction and thus this paper presents a model of PM2.5 prediction based on image contrast-sensitive features. Two types of features were extracted from the images and utilized to estimate the PM2.5 concentration. Then, we establish a recurrent fuzzy neural network model, the parameters of which are trained by using the gradient descent algorithm with an adaptive learning rate. Experiment results indicate that the recurrent neural network has better prediction performance than traditional radial basis function and fuzzy neural network.