## 3 Results
### Classification of Fires  
The effectiveness of ANNs to classify fires in each zone during the studied period was applied to the database described in Section 2 and with the variables shown in Table 2. The subset of fires larger than 30,000 m\({}^{2}\) (big fires) was extended with the oversampling technique to balance with the fires smaller than 30,000 m\({}^{2}\) (small fires) data subset. The experiments demonstrated the lesser importance of coordinates, altitude, and vegetation in each of the four zones, so these variables were removed in subsequent analysis. AI only showed its relevance in Zone 4, so it was used just for this area. Thus, the variables considered in the study were finally: month, H, T, V, FWI and, in Zone 4, also AI. It is important to note that Precipitation is part of both FWI and AI indices. In order to improve the robustness and stability of results and to reduce bias, five partitions in the dataset were made (50-50%, 60-40%, 70-30%, 80-20%, and 90-10% in training-test, respectively). The obtained classification accuracy and values of sensitivity and specificity by the ANN classifier for size of wildfire with mentioned partitions are shown in Table 7. Sensitivity defines the proportion of true positive big fires detected in the total group of real big fires. Actually, it can be considered as \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Zone** & **Period** & **Total Number of Fires** & **Average Fires Per Year** \\ \hline \multirow{3}{*}{Zone 1} & 2000–2014 & 661 & 44.03 \\  & 2071–2100 RCP4.5 & 1981 & 66.00 \\  & 2071–2100 RCP8.5 & 2632 & 87.68 \\ \hline \multirow{3}{*}{Zone 2} & 2000–2014 & 614 & 44.90 \\  & 2071–2100 RCP4.5 & 2437 & 81.18 \\  & 2071–2100 RCP8.5 & 3170 & 105.60 \\ \hline \multirow{3}{*}{Zone 3} & 2000–2014 & 1166 & 77.68 \\  & 2071–2100 RCP4.5 & 2222 & 74.02 \\  & 2071–2100 RCP8.5 & 2933 & 97.70 \\ \hline \multirow{3}{*}{Zone 4} & 2000–2014 & 552 & 36.77 \\  & 2071–2100 RCP4.5 & 2396 & 79.82 \\ \cline{1-1}  & 2071–2100 RCP8.5 & 3177 & 105.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of historical (2000–2014) and predicted (2071–2100, both RCP4.5 and RCP8.5 scenarios) number of fires in the region. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Zone** & **Period** & **Py (mm/year)** & **H (\%)** & **T (\({}^{\circ}\)C)** & **V (m/s)** \\ \hline \multirow{3}{*}{Zone 1} & 2000–2014 & 252.67 & 59.13 & 16.56 & 1.06 \\  & 2071–2100 RCP4.5 & 350.67 & 57.96 & 18.09 & 1.24 \\  & 2071–2100 RCP8.5 & 328.92 & 56.79 & 20.04 & 1.21 \\ \hline \multirow{3}{*}{Zone 2} & 2000–2014 & 341.42 & 61.08 & 15.84 & 1.96 \\  & 2071–2100 RCP4.5 & 350.67 & 57.96 & 20.49 & 2.49 \\  & 2071–2100 RCP8.5 & 328.92 & 56.79 & 21.89 & 2.43 \\ \hline \multirow{3}{*}{Zone 3} & 2000–2014 & 264.23 & 58.62 & 17.88 & 1.39 \\  & 2071–2100 RCP4.5 & 350.67 & 57.96 & 19.54 & 1.75 \\  & 2071–2100 RCP8.5 & 328.92 & 56.79 & 21.29 & 1.69 \\ \hline \multirow{3}{*}{Zone 4} & 2000–2014 & 214.06 & 65.87 & 18.57 & 1.79 \\  & 2071–2100 RCP4.5 & 350.67 & 57.96 & 20.70 & 2.08 \\ \cline{1-1}  & 2071–2100 RCP8.5 & 328.92 & 56.79 & 22.34 & 2.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of average meteorological data between available historical wildfire data from the period 2000–2014 and future climate change scenarios data (RCP4.5 and RCP8.5) during the period (2071–2100) (Py is average yearly rainfall, H is humidity, T is temperature, and V is wind velocity). the probability of getting a positive test result in big fire datasets. Hence, it relates to the potential of the test to recognize big fires. All regions showed an average sensitivity percentage higher than 60%, even reaching 70% in Zone 3, but in Zone 4 the average reached around 55%. This was likely due to the fact that Zone 4 is the most populated zone (coastal area) of the region and lower values of sensitivity might indicate a greater relevance of anthropogenic causes. The same pattern with higher values, especially in Zone 4, is shown for the specificity measure.