## 2 Related Work
This approach is applied to examine possibilities of having the best forecasting precision by contrasting four different supervised machine learning algorithms (decision tree, random forest tree, Naive Bayes theorem, and K-nearest neighbor) for prediction calculations. Data sets from all Indian states were collected and used as the research sample. The trained extreme learning machine has been applied to eight air quality parameters, obtained from two Hong Kong monitoring stations over a six-year period [30]. This approach enabled the prediction of the concentration of air pollutants at an extremely fast learning speed and resulted in an appropriate level of prediction accuracy. The work of [PERSON] et al. implements air pollution prediction based on the creation and application of neural networks [31]. The obtained data for model creation have been collected on an hourly basis for five air pollutants (SO\({}_{2}\), CO, NO\({}_{2}\), NO, and O\({}_{3}\)) at six locations in the area of Bilbao, Spain, during the year 2000, and the prediction models were tested upon the data from 2001. The created prediction models show diversity in accuracy when comparing different pollutants and measuring sensors. [PERSON] et al. present a novel spatiotemporal interpolation model that combines data fusion techniques with a Long Short-Term Memory (LSTM) recurrent neural network (RNN) in order to achieve high estimation accuracy over a long time period [44]. Data fusion is performed upon the meteorological data, elevation data, land-use data, and daily PM2.5 data collected from China in 2016, to be used within four experiments to evaluate the efficiency and effectiveness of the proposed approach. The linear regression method is implemented within the applied artificial intelligence methods and tools. [PERSON] et al. present the system that enables weather and air quality forecasts, based on the use of linear regression and neural networks [28]. [PERSON] et al. present results in the forecasting of the Air Quality Index (AQI) by using machine learning techniques: linear regression and time series analysis with supervised machine learning [32]. Sensor output was related to NO\({}_{2}\), ozone (O\({}_{3}\)), PM2.5, and SO\({}_{2}\) data concentrations, and they were used to train a regression model. The obtained prediction model was validated with new sensor output data, and the performance has been analyzed. The Auto Regressive Integrated Moving Average (ARIMA) time series model is applied to forecast the AQI. [PERSON] and [PERSON] present the results of an application of the fuzzy linear regression method, with data collected daily from three air quality monitoring stations in Baghdad City [33]. [PERSON] et al. propose a combination of linear regression and genetic algorithm methods for application, as well as multivariate polynomial regression [34]. Linear regression is used for the prediction of gas concentration, while a genetic algorithm approach is used to optimize results, i.e., to minimize errors in linear regression prediction. Predictive equations are formed for CO, O\({}_{3}\), and NO\({}_{2}\) based on data values for temperature, relative humidity, benzene, and NO\({}_{x}\). Some research results are related to the application of the linear regression method to the prediction of particular air pollutant concentrations. A multivariate linear regression model was proposed to enable short-term period prediction of PM2.5 based on data on aerosol optical depth (AOD) obtained through remote sensing, meteorological factors from ground monitoring (wind velocity, temperature, and relative humidity), and other gaseous pollutants (SO\({}_{2}\), NO\({}_{2}\), CO, and O\({}_{3}\)) [32]. The validity of the derived regression models has been measured, and it has been shown that annual data predictions have a lower fit compared to seasonal predictions (spring and winter data), which are more accurate.