Abstract
Due to the randomness and uncertainty in the atmospheric environment, and accompanied by a variety of unknown noise. Accurate prediction of PM2.5 concentration is very important for people to prevent injury effectively. In order to predict PM2.5 concentration more accurately in this environment, a hybrid modelling method of support vector regression and adaptive unscented Kalman filter (SVR-AUKF) is proposed to predict atmospheric PM2.5 concentration in the case of incorrect or unknown noise. Firstly, the PM2.5 concentration prediction model was established by support vector regression. Secondly, the state space framework of the model is combined with the adaptive unscented Kalman filter method to estimate the uncertain PM2.5 concentration state and noise through continuous updating when the model noise is incorrect or unknown. Finally, the proposed method is compared with SVR-UKF method, the simulation results show that the proposed method is more accurate and robust. The proposed method is compared with SVR-UKF, AR-Kalman, AR and BP methods. The simulation results show that the proposed method has higher prediction accuracy of PM2.5 concentration.
Highlights
The rapid development of our industrialization process and economy in recent years, air pollution has already affected in developed regions of China such as Beijing, Tianjin and Hebei, the Pearl River Delta and the Yangtze River Delta.[1]
The comparison results are given as follows: Figure 3 shows the prediction results based on SVRAUKF method and support vector regression (SVR)-unscented Kalman filter (UKF) method with incorrect noise statistics
When the inaccurate measurement noise is given, the SVR-adaptive unscented Kalman filter (AUKF) method still has higher prediction accuracy, and it is not affected by the noise, which can be estimate adaptively
Summary
The rapid development of our industrialization process and economy in recent years, air pollution has already affected in developed regions of China such as Beijing, Tianjin and Hebei, the Pearl River Delta and the Yangtze River Delta.[1]. When the statistic noise is assumed to set, the SVR-UKF method can predict PM2.5 concentration accurately. The comparison results are given as follows: Figure 3 shows the prediction results based on SVRAUKF method and SVR-UKF method with incorrect noise statistics.
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