# Contributing towards Representative PM Data Coverage by Utilizing Artificial Neural Networks
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{3}{c}{**PM\({}_{10}\)**} & \multicolumn{3}{c}{**PM\({}_{2.5}\)**} \\ \cline{2-7}  & **Mean** & **Max** & **Min** & **Mean** & **Max** & **Min** \\ \hline
2016 & 21.70 & 714 & 1 & 12.30 & 208 & 0 \\ \hline
2017 & 16.83 & 136 & 1 & 10.73 & 67 & 0 \\ \hline
2018 & 19.85 & 530 & 1 & 11.60 & 118 & 0 \\ \hline Total & 19.43 & 714 & 1 & 11.54 & 208 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Yearly Mean, Max and Min values of PM\({}_{10}\) and PM\({}_{2.5}\) concentrations in the AGP monitoring station for the period 2016–2018. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Training** & \
\begin{tabular}{c} **PM\({}_{10}\)** \\ **Validation** \\ \end{tabular} & **Test** & **Training** &
\begin{tabular}{c} **PM\({}_{2.5}\)** \\ **Validation** \\ \end{tabular} & **Test** \\ \hline PM & 82,712 & 17,720 & 17,720 & 55,450 & 11,880 & 11,880 \\ PM + T & 93,051 & 19,935 & 19,935 & 66,540 & 14,256 & 14,256 \\ PM + WS & 93,051 & 19,935 & 19,935 & 66,540 & 14,256 & 14,256 \\ PM + T + WS & 103,390 & 22,150 & 22,150 & 77,630 & 16,632 & 16,632 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of data points in each subset for PM\({}_{10}\) and PM\({}_{2.5}\) and the four different input cases. the results of the FFNNs method, Table 5 provides some interesting findings. In general, the PM\({}_{10}\) models are associated with low error and high correlation values, providing satisfactory results regardless of the input data that were used. The PM\({}_{2.5}\) models' results showcase higher MAE error values (considering that the MAE error is higher when compared with the average PM\({}_{2.5}\) values) and lower \(R^{2}\) values (which can possibly be attributed to the lower number of input data, and subsequently, less information during the training process). However, on average, in both cases, the schemes that include T and WS values give lower MAE and higher \(R^{2}\) values. This is evident especially for the two models that include both T and WS, where the lowest MAE (3.67 \(\upmu\)g/m\({}^{3}\) and 2.39 \(\upmu\)g/m\({}^{3}\)) and highest \(R^{2}\) (0.94 and 0.75) values are produced. Although the MAE value average for the models with PM\({}_{10}\) inputs (3.85 \(\upmu\)g/m\({}^{3}\)) is higher than the corresponding value for PM\({}_{2.5}\) (2.44 \(\upmu\)g/m\({}^{3}\)), the MAE statistical metric uses the same scale as the data being measured and is not suitable for comparison between PM\({}_{10}\) and PM\({}_{2.5}\) in contrast to \(R^{2}\), which illustrates better results for the PM\({}_{10}\) cases. A conclusion of significant importance can be drawn by comparing the MAE values with the yearly mean, maximum and minimum concentrations, which are presented in Table 2. While the FFNNs that include as predictors the surface temperature and wind speed correspond to better performance statistics (lower MAE and higher \(R^{2}\)), the differences between the models are small regarding the Table 2 values. This fact illustrates the effectiveness, in this case, of the models that are using only concentrations from neighbouring stations. However, adding more parameters or changing the networks configuration (i.e., selecting the subsets data by chronological order and not randomly, using different approaches to avoid overfitting etc.) can further improve the results. An additional evaluation of the FFNN models is performed by plotting scatter diagrams of the predicted versus the observed values, as presented in Figure 4.