An Adaptive Kalman Filtering Approach to Sensing and Predicting Air Quality Index Values

Chen, Jibo; Chen, Keyao; Ding, Chen; Wang, Guizhi; Liu, Qi; Liu, Xiaodong

Abstract

In recent years, Air Quality Index (AQI) have been widely used to describe the severity of haze and other air pollutions yet suffers from inefficiency and compatibility on real-time perception and prediction. In this paper, an Auto-Regressive (AR) prediction model based on sensed AQI values is proposed, where an adaptive Kalman Filtering (KF) approach is fitted to achieve efficient prediction of the AQI values. The AQI values were collected monthly from January 2018 to March 2019 using a WSN-based network, whereas daily AQI values started to be collected from October 1, 2018 to March 31, 2019. These data have been used for creation and evaluation purposes on the prediction model. According to the results, predicted values have shown high accuracy compared with the actual sensed values. In addition, when monthly AQI values were used, it has depicted higher accuracy compared to the daily ones depending on the experimental results. Therefore, the hybrid AR-KF model is accurate and effective in predicting haze weather, which has practical significance and potential value.

Citation

Chen, J., Chen, K., Ding, C., Wang, G., Liu, Q., & Liu, X. (2020). An Adaptive Kalman Filtering Approach to Sensing and Predicting Air Quality Index Values. IEEE Access, 8, 4265-4272. https://doi.org/10.1109/access.2019.2963416

• Journal Article Type: Article
• Acceptance Date: Dec 29, 2019
• Online Publication Date: Jan 1, 2020
• Publication Date: Jan 1, 2020
• Deposit Date: Jan 24, 2020
• Publicly Available Date: Jan 24, 2020
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Pages: 4265-4272
• DOI: https://doi.org/10.1109/access.2019.2963416
• Keywords: Real-time sensing and predicting, Kalman filter, air quality index, simulation
• Public URL: http://researchrepository.napier.ac.uk/Output/2501500