## 1 Introduction  
Air pollution is a major problem in public health that increases health impacts on both the cardiovascular and respiratory systems in humans [1]. There are many important air pollutants, including ground-level ozone (O\({}_{3}\)), carbon monoxide (CO), nitrogen dioxide (NO\({}_{2}\)), sulfur dioxide (SO\({}_{2}\)), and particulate matter (PM), announced by the World Health Organization. However, PM exceeds both the national and international standards to the greatest extent compared with others [2]. The PM is a mixture of particles that it compounds and four types of components, namely, organic, inorganic, biological, and carbonaceous materials. The proportion of each component is different in each area [3]. Most of the PM is classified into two categories by size, which are based on health-related effects [4]. The size of PM affecting human health has an aerodynamic diameter of less than 10 \(\upmu\)m, which can only be detected by an electron microscope. There are two major sizes of PM. First, coarse particulate matter called PM\({}_{10}\) is PM with an aerodynamic diameter smaller than 10 \(\upmu\)m. Another type is fine particulate matter called PM\({}_{2.5}\), which is PM with an aerodynamic diameter smaller than 2.5 \(\upmu\)m [5,6]. However, there are other types of PM, such as PM\({}_{1}\)[7], which are excluded from this research due to air pollution standards. Every year during the dry season, which begins in February, the upper northern region of Thailand is affected by air pollution problems from both types of PM and this problem ends when the rainy season begins [8]. Anthropogenic activities, both garbage and agricultural burning, are important sources that contribute to air pollution. After the harvest periods, farmers prepare their area for the next crop period by burning their crop residues [9]. Another source is wildfire from natural and human-made occurrences as this area is mostly covered with forests and mountains. Fire management is difficult due to many limitations, such as a lack of effective equipment [10]. There are many policies from the government to protect and prohibit burning. However, the air pollution problem does not seem to be improved. In recent years, researchers have been focused on both processes and methods in data science to apply it in various applications, such as daily cattle health classification [11], tomography image analysis [12], and student dropout prediction [13]. For the air pollution problem, data science techniques can implement notification systems to alert people by predicting the upcoming air pollution level. Numerous research articles are interested in applying data science to the air pollution problem, especially both types of PM. They try to find both appropriate processes and methods to create prediction models with high model performance or computation time reduction for their desired output, such as PM concentrations, PM levels, or classes [14; 15; 16]. The popular models are multiple linear regression (MLR), autoregressive integrated moving average (ARIMA), and various types of artificial neural networks (ANNs). MLR is a popular statistical model for comparing the model performance with the ANN, but the results showed that MLR is less effective than ANN [17; 18; 19; 20]. ARIMA is a common model for time-series data. There are two interesting examples. The first example, a combination of MLR and ARIMA proposed by [21] was used to predict daily and monthly average PM\({}_{10}\) concentrations in Delhi, India. The second example, using the output data from ARIMA as input features for MLR, was presented by [22]. In the article, ARIMA is used with the dataset, including seasonal features and the period of seasonal patterns, to predict hourly PM\({}_{10}\) concentrations in Negeri Sembilan, Malaysia. The ANN is the most popular model selected by many researchers as it outperforms other models.