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Causal Identification Based on Compressive Sensing of Air Pollutants using Urban Big Data
2020
IEEE Access
This study addresses the causal identification of air pollutants from surrounding cities affecting Beijing's air quality. A novel compressive sensing causality analysis (CS-Causality) method, which combines Granger causality analysis (GCA) and maximum correntropy criterion (MCC), is presented for efficient identification of the air pollutant causality between Beijing and surrounding cities. Firstly, taking the spatiotemporal correlation into consideration, the original data is mapped into
doi:10.1109/access.2020.3000767
fatcat:by5mteimsjgujm772c6eoxgzle