Forecasting the air quality using OWA based time series model

The environmental protection conception increasing, the prediction of air quality is more and more important. The main Pollutant Standards Index (PSI) includes PM₁₀, SO₂, NO₂, CO and O₃ etc... The PSI will be produced and changed when combining in the air. Due to the concentrations of CO, SO₂, NO₂, and PM₁₀ have declined, the focus of health studies and control efforts has increasingly turned to PM₁₀ and O₃ as the most important air pollutant species of concern. Correspondingly, the primary focus on the current understanding of the health is affected by PM₁₀ and O₃ in the Taiwan. Therefore, this study uses O₃ attribute to evaluate air quality. This paper proposes an OWA based time series model to predict the air quality. Due to O₃ data is belong to time series pattern, and OWA operator can aggregate multiple lag periods into single aggregated value by different situation parameters alpha. Based on the advantages of TSM and OWA, the OWA based time series model can efficiently and accurately predict PSI. In verification, this paper collects a practical data to verify the proposed method. The dataset contains records of 1061 days with O₃ attribute from air qualities inspection station in Hsinchu city, Taiwan. From the results, the proposed method outperforms the listing methods.