## 5 Conclusions  
The objective of this paper is to give a general perception of the current approaches presented related to the air quality prediction concept by reviewing the recent publications. As air quality prediction is a huge topic, we have defined a set of key points in order to narrow the scope and focus on a specific task. To select papers, we inserted a beforehand defined query in the following databases: _Scopus_ and _IEEE Xplore_ repositories. For further observation, we have selected studies published since 2002 and, afterwards, by excluding irrelevant papers based on the inclusion/exclusion criteria. Eventually, 41 manuscripts were selected. Reviewing the chosen papers, we have extracted the essential features and based on the latter findings, the papers were linked, and further comparison was carried out. Taking into consideration the geographical component, the result shows that China was the leading country being the case study in 26 papers. The important finding was that to increase the accuracy of air quality prediction, it is valuable, in addition to the air quality data, to include also a dataset of other factors that affects the air quality. Thus, in most cases, the authors used meteorological data, and some of them also involved other types of data, such as calendar features, traffic intensity features, spatial features, etc. Related to the prediction target, the outcome shows that PM\({}_{2.5}\) was the main element, applied in 19 papers, 18 of which utilised data of the cities located in China. Most cases, the authors performed a prediction for the next day. Twenty-seven studies used data hourly collected from the sensors. Among the analysed works, 20 of them use open data to perform air quality predictions. These works were carried out from 2014 until now, coinciding with the movement of open data within the cities [70]. Therefore, we can affirm that the open data movement has increased the number of research works in the field of machine learning, especially in the prediction of air quality. Regarding machine learning techniques, the studies used neural networks (38%), regression(24%), ensemble (22%), hybrid (11%) models, one study used regularization and optimization, and the other research applied multinomial naive bayes and multinomial logistic regression methods. For evaluating applied techniques tailored to the algorithms mentioned above, different metrics were applied. Overall, 29 metrics were applied, from which MAE and RMSE were the most used metrics, each of them being applied in 24 papers. It is very important to mention the challenges regarding the data. First of all, for an accurate air quality prediction it is essential to capture as much relevant data as possible, including weather forecast data, air quality data, meteorological data, etc. Then, it is necessary to apply different techniques (e.g., PCA) to remove redundant data and to select representative subsets for further analysis. It is also important to mention that air quality prediction for a long temporal resolution is a challenging task, as the accuracy decreases with the increase of the prediction interval. Another essential aspect is time complexity; for example, methods based on neural network algorithms to train data, usually require a long time. In general, it is very difficult to compare the results obtained during analysis of the papers, as they used different data, and they analysed different temporal granularity. As future work, an exhaustive \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Work** & **Algorithms** & **MAE\_24h** & **RMSE\_24h** \\ \hline
[13] & NN & 8.49 & 12.03 \\
[17] & NN & 34.35 & \(N/S\) \\
[22] & NN & 45.1\(\pm\)0.1 & \(N/S\) \\
[29] & NN & 5.5 & 6.9 \\
[30] & NN & \(N/S\) & 24.06 \\
[52] & Ensemble & 26.44 & \(N/S\) \\
[55] & Ensemble & 34.25 & \(N/S\) \\
[67] & \(N/S\) & \(N/S\) & 0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PM\({}_{2.5}\) prediction accuracy for the next 24 h measured by mean absolute error (MAE) and root mean square error (RMSE).