Atmospheric PM_2.5 concentration prediction and noise estimation based on adaptive unscented Kalman filtering

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.

Ensemble Deep Learning for Classification of Pollution Peaks

“…The highest accuracy is obtained for O3, CO and NO 2 (above 0.75). As already reported in other studies, SO 2 and PM 2.5 are slightly more difficult to predict [34], [35]. According to the F1 and AUC metrics, we can conclude that the EDL outperforms both the traditional machine learning algorithm (GBM) and simple deep learning methods (LSTM and BiRNN) whatever the pollutants are considered.…”

Short Path Wind-Field Distance-Based Lagrangian Trajectory Model for Enhancing Atmospheric Dispersion Prediction Accuracy

“…It refers to the transport of pollutants through the air, driven by wind field, temperature gradients, and other meteorological conditions. Atmospheric dispersion models are used to forecast the movement and behavior of pollutants in the atmosphere, to help develop effective strategies for lowering air pollution and improving air quality, especially in industrial areas [9][10][11][12][13][14][15]. One way to examine the movement of air pollutants, such as PM2.5, is through Lagrangian trajectory models.…”