An improved picture-based prediction method of PM2.5 concentration

PM2.5 can bring serious harm to people's health and life because it easily causes cardiovascular disease and increases the risk of cancer. Hence, monitoring PM2.5 real-timely becomes a key problem in environmental protection. Towards this end, this paper proposes an improved picture-based prediction method of PM2.5 concentration using artificial neural network (ANN). Firstly, the weather image is transformed into Hue, Saturation, Value (HSV) color space to extract its saturation map, then the corresponding spatial and transform-based entropy features of image space are extracted. Secondly, the PM2.5 concentration model is built based on the two extracted features from the weather image using Artificial Neural Network (ANN) theory. Thirdly, an ANN model is trained using the pre-processed data. The training parameters and conditions are also explored through multiple experiments to achieve the best model accuracy. Experimental results show that the model has the best prediction effect when comparing to other state-of-the-art models.