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

Chu, Junwen; Dong, Yingchao; Han, Xiaoxia; Xie, Jun; Xu, Xinying; Xie, Gang

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

2021

PM₂.₅ (particulate matter with a size/diameter ≤ 2.5 μm) is an important air pollutant that affects human health, especially in urban environments. However, as time-series data of PM₂.₅ are non-linear and non-stationary, it is difficult to predict future PM₂.₅ distribution and behavior. Therefore, in this paper, we propose a hybrid short-term urban PM₂.₅ 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 PM₂.₅ 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.

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