## 3 Results
### Exhaustive Descriptions of Included Studies
#### 3.2.1 Group 1: Neural Network (NN)
The proposed model was compared to the following methods: ARIMA, lasso, GBDT, FFA [23], LSTM, DeepST [24], DMVST-Net [25], DeepSD [26], DeepFM [27], WFM [28]. As an evaluation, metrics accuracy (ACC) and MAE were selected. The proposed approach outperformed other methods. The final results had 2.4%, 12.2%, and 63.2% relative accuracy improvements on short-term, long-term and sudden changes prediction, respectively compared to the previous system. Regarding future work, the long-term sudden changes prediction was considered. _Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong [29]_: to increase air pollution prediction [PERSON] and [PERSON] applied the extreme learning machine which performed good generalization with fast learning speed. The dataset used in this study included air quality (NO\({}_{2}\), nitrogen oxide (NO\({}_{x}\)), O\({}_{3}\), PM\({}_{2.5}\), SO\({}_{2}\)) and meteorological (temperature, wind speed, wind direction, relative humidity) data during the period of 2010-2015. The following parameters were applied in order to evaluate the proposed methods: MAE, RMSE, IA, and R\({}^{2}\). Compared to FeedForward Neural Network based on Back Propagation (FFANN-BP) and Multiple Linear Regression (MLR), the proposed method performed better. _Relevance analysis and short-term prediction of PM\({}_{2.5}\) concentrations in Beijing based on multi-source data [30]_: focuses on the short-term prediction of PM\({}_{2.5}\) in Beijing. Multivariate statistical analysis method and Back Propagation Neural Network (BPNN) were applied in order to study correlation analysis. Afterwards, ARIMA was applied for predicting PM\({}_{2.5}\). The dataset consisted of air quality data (CO, NO\({}_{2}\), SO\({}_{2}\), PM\({}_{10}\)), meteorological data (average rainfall, daily mean temperature, average relative humidity, average wind speed, maximum wind speed) and social media data (microblog data) collected during January and December in 2014. To analyse the correlation, the authors used R and the Spearman Correlation Coefficient and to evaluate the method RMSE was applied, which reached to 6.76 \(m\)g/m\({}^{3}\). _Evolving Keras Architectures for Sensor Data Analysis [31]_: presents the genetic algorithm for the architecture of deep neural networks using the KERAS library [32]. Applying air pollution data, the results showed that the proposed model could increase the accuracy of the air pollution prediction. The target pollutants were CO, NO\({}_{2}\), NO\({}_{x}\), benzene (C\({}_{6}\)H\({}_{6}\)), and non-methane hydrocarbons (NMHC). Compared to SVM and selected fixed architectures, the proposed method performed better. _Forecasting PM\({}_{2.5}\) Concentration using Spatio-Temporal Extreme Learning Machine [33]_: by taking into account fast training, fewer configuration parameters, and ease of obtaining global optima, Spatio-Temporal Extreme Learning Machine (STELM) method was applied for enhancing forecasting of PM\({}_{2.5}\) for the next 72 hours. The dataset consisted of air quality data (NO\({}_{2}\), CO, SO\({}_{2}\), O\({}_{3}\), PM\({}_{10}\), and PM\({}_{2.5}\)) and meteorological spatio-temporal sequences (temperature, humidity, wind direction, wind force, and precipitation) collected during April and May in 2014. Mean Relative Error (MRE) and MAE were used to evaluate the proposed method. Overall, the precision in the first 12, 24, 48, 72 hours were 82%, 78%, 71%, and 63%, respectively. _Urban Air Pollution Monitoring System With Forecasting Models [34]_: aims to monitor urban air pollution and based on the results to make a prediction. [PERSON] et al. applied the following machine learning algorithm, including SVM, M5P, and ANN with univariate and multivariate models to forecast O\({}_{3}\), NO\({}_{2}\), and SO\({}_{2}\) for the next 1, 8, 12, and 24 h. The data were collected every 15 min. To compare the methods, the following metrics were used, including Prediction Trend Accuracy(PTA), RMSE and NRMSE. The results showed that M5P outperformed other methods.