A spatiotemporal model for PM2.5 prediction based on the K‐Core idea and label distribution

Abstract

Abstract With the increasingly severe problem of PM2.5 environmental pollution, the threat to human health is gradually increasing. Therefore, accurate prediction of PM2.5 concentration is of great significance to the healthy life of human beings. To make up for the deficiencies of previous studies on PM2.5 concentration prediction, a spatiotemporal model (Spatiotemporal prediction model of label distribution, LDSPM) for PM2.5 concentration prediction based on the K‐Core algorithm concept and label distribution learning was proposed. Leveraging K‐Core ideas and the label distribution support vector regression model of the label distribution paradigm, the influence weight of each meteorological factor on PM2.5 concentration in each piece of data was obtained with the decomposition of meteorological factors using the complete ensemble empirical mode decomposition of adaptive noise. Using a long short‐term memory neural network to predict each decomposed signal and obtain the forecast data of meteorological factors. Finally, according to the expected weight and meteorological factor data, a particle swarm optimization extreme learning machine is used to train the prediction, and the predicted value of PM2.5 is obtained. The experimental results show that the forecasting model performs better than other combined and single forecasting models. It provides new directions and ideas for PM2.5 concentration prediction.

Keywords

• complete ensemble empirical mode decomposition of adaptive noise
• extreme learning machine
• label distribution learning
• long short‐term memory
• PM2.5