**2. Related works**

In this section, we analyzed related studies to predict the concentration of fine dust [7–12]. The related studies use air pollution data and meteorological data together. In particular, the accuracy of prediction is high when weather data such as temperature and wind speed are used rather than air pollution data [7]. Traditionally, the studies predict the concentration of fine dust through machine learning methods such as linear regression or support vector regression. However, these methods are challenging to consider the spatiotemporal correlation [8]. Therefore, it focuses on improving prediction accuracy by using deep learning [9–12]. There are four distinct seasons in Korea depending on the air mass, so there is a significant difference in the concentration of fine dust by season. Therefore, we must be considered the relationship between location and time.

Joun et al. predicted the concentration of fine dust using the MLR, SVR, ARIMA, and ARIMAX [11]. In this paper, the training datasets are air pollution data (NO2, SO2, CO, O3, PM10) and meteorological data (temperature, precipitation, wind speed). They confirmed that time, location, NO2, CO, O3, SO2, maximum temperature, precipitation, and maximum wind speed were significant variables using multiple linear regression analysis. In addition, they used multiple linear regression and support vector regression to predict fine dust distribution. The prediction accuracy was higher in the artificial neural network than in the multiple support vector regression. If the PM10 concentration increased above 100, the support vector regression was exceptionally high. They perform experiments using ARIMA and ARIMAX to analyze the factors of time according to the location. As a result, there was a difference in the learning accuracy according to the location of the experimental data. Furthermore, the accuracy was higher in using the air quality factor and the meteorological factor than using only the time variable.

#### *Practical Application Using the Clustering Algorithm DOI: http://dx.doi.org/10.5772/intechopen.99314*

Cho et al. designed a predictive model through multiple linear regressions and artificial neural networks and performed the fine-dust prediction [12]. They collected the training data, air pollution data (NO2, SO2, CO, O3, PM10), and meteorological data (temperature, humidity, wind speed, wind direction).

As a result of analyzing the errors by performing prediction, the accuracy of the prediction model using artificial neural networks was better overall than that of multiple linear regression. As the result of the experiment by changing the hidden layers of the artificial neural network, the performance of the multi-layer perceptron was better when there were three hidden layers.
