Modelling the Effect of Calendar Variation in the GSTARIMAX For Predicting Nitrogen Monoxide Air Quality

Hani Khaulasari, Jeneiro Rezkyansyah Maulana Akbar

Abstract


Nitrogen monoxide (NO) pollution has had a devastating impact on the environment and public health in Surabaya. This study aims to determine the best prediction model and forecast nitrogen monoxide concentrations in the April 2024 period. The method used is the GSTARIMAX model, which integrates the influence of calendar variation as well as spatial weight. Calendar factors such as school holidays, Christmas, New Year, and Eid al-Fitr are included as pseudo-exogenous variables (dummy). Data was obtained from three air quality monitoring points in Surabaya, namely SPKU Wonorejo, Kebonsari, and Tandes, throughout January 2023 to March 2024. Parameter estimation in the GSTARIMAX model used the Generalized Least Squares (GLS) and Ordinary Least Squares (OLS) approaches. This study also compares three types of spatial weights and compares the performance of the GSTARIMAX model with other models that consider or ignore calendar variations. The results of the analysis show that significant parameters are derived from the AR(1) model, so that the GSTARIX-SUR(1) model with first-order spatial lag and cross-normalized correlation weight provides the best performance, indicated by the sMAPE value below 10% and the lowest RMSE value. In addition, this model also meets the assumptions of white noise and normal distribution. Fluctuations in nitrogen monoxide concentrations during April 2024 show fairly high volatility, with a significant spike occurring on April 12–14, 2024. The increase is correlated with the return flow of people from outside the city to Surabaya after the Eid al-Fitr holiday.

Keywords


Air quality predict; calendar variation; GSTARIMA; GSTARIX-SUR; GSTARIX-OLS; nitrogen monoxide

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References


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DOI: https://doi.org/10.37905/euler.v13i3.33830

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