Atmospheric PM_2.5 Prediction Based on Multiple Model Adaptive Unscented Kalman Filter

The PM${}_{2.5}$ concentration model is the key to predict PM${}_{2.5}$ concentration. During the prediction of atmospheric PM${}_{2.5}$ concentration based on prediction model, the prediction model of PM${}_{2.5}$ concentration cannot be usually accurately described. For the PM${}_{2.5}$ concentration model in the same period, the dynamic characteristics of the model will change under the influence of many factors. Similarly, for different time periods, the corresponding models of PM${}_{2.5}$ concentration may be different, and the single model cannot play the corresponding ability to predict PM${}_{2.5}$ concentration. The single model leads to the decline of prediction accuracy. To improve the accuracy of PM${}_{2.5}$ concentration prediction in this solution, a multiple model adaptive unscented Kalman filter (MMAUKF) method is proposed in this paper. Firstly, the PM${}_{2.5}$ concentration data in three time periods of the day are taken as the research object, the nonlinear state space model frame of a support vector regression (SVR) method is established. Secondly, the frame of the SVR model in three time periods is combined with an adaptive unscented Kalman filter (AUKF) to predict PM${}_{2.5}$ concentration in the next hour, respectively. Then, the predicted value of three time periods is fused into the final predicted PM${}_{2.5}$ concentration by Bayesian weighting method. Finally, the proposed method is compared with the single support vector regression-adaptive unscented Kalman filter (SVR-AUKF), autoregressive model-Kalman (AR-Kalman), autoregressive model (AR) and back propagation neural network (BP). The prediction results show that the accuracy of PM${}_{2.5}$ concentration prediction is improved in whole time period.