Short-term prediction of urban PM2.5based on a hybrid modified variational mode decomposition and support vector regression model

PM2.5(particulate matter with a size/diameter <= 2.5 mu m) is an important air pollutant that affects human health, especially in urban environments. However, as time-series data of PM(2.5)are non-linear and non-stationary, it is difficult to predict future PM(2.5)distribution and behavior. Therefore, in this paper, we propose a hybrid short-term urban PM(2.5)prediction model based on variational mode decomposition modified by the correntropy criterion, the state transition simulated annealing (STASA) algorithm, and a support vector regression model to overcome the disadvantages of traditional forecasting techniques which consider different environmental factors. Two experiments were performed with the model to assess its effectiveness and predictive ability: in experiment I, we verified the performance of STASA on benchmark functions, while in experiment II, we used PM(2.5)data from different epochs and regions of Beijing to verify its forecasting performance. The experimental results showed that the proposed model is robust and can achieve satisfactory prediction results under different conditions compared with current forecasting techniques.