## 3 Results
### Burned Area Prediction under Climate Change  
After having classified wildfires into two main classes (big and small wildfires), an ANN was used to predict fires of burned area equal or larger than 30,000 m\({}^{2}\). In this case, the dataset was divided into three sections. Sixty percent of the data was used for training purposes, 20% was used for validation, and the remaining 20% was reserved for testing [10]. The variables considered in prediction were M, H, T, V, FWI, and AI. Table 9 shows the results of all metrics considered in each region for all the partitions described. MAE and RMSE values are higher, and relatively similar, in Zones 1 and 4 but total burned areas in these zones are significantly different, practically double that in Zone 4. In fact, the sum of RMSE in the three partitions represents 5.64% and 1.85% of the total wildfire surface for big fires in Zones 1 and 4, respectively. Likewise, Zones 2 and 4 have a similar behavior for the same metrics and similar percentage of sum of RMSE to burned surface, resulting in 3.77% and 3.62% in Zones 2 and 4, respectively, since the burned area in big wildfires in these zones differs by less than 20%. MAPE percentages range from 11% to 43% in the training set and from 19% to 40% in the test set. Zone 3 remains invariable in partitions within the range of 12-20%, whereas the rest of the zones show wider variations, especially Zone 2. Nonetheless, test values do not exceed 40% or even reach around 20% in Zones 1 and 3. Concerning bias, the ANN model tends to underestimate burned surface by around 20% on average for the whole region, though it varies, both overestimating and underestimating, depending on the partition and zone under study. Burned surface over the long-term period (2071-2100) increases hugely in all zones (Table 10). Average yearly values are up to 12 times (or even more depending on the climate scenario) higher than recorded wildfires in the period under study (2000-2014) in Zone 3. Increases  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Zone** & **Measure** & **50–50\%** & **60–40\%** & **70–30\%** & **80–20\%** & **90–10\%** & **Average** \\ \hline \multirow{3}{*}{Zone 1} & Sensitivity (\%) & 57.2 & 58.5 & 60.9 & 63.6 & 77.1 & 63.46 \\  & Specificity (\%) & 62.3 & 57.8 & 64.1 & 70.2 & 62.5 & 63.38 \\  & Accuracy (\%) & 59.1 & 58.1 & 62.4 & 65.9 & 70.1 & 63.1  
rest of the zones are lower, but they are up to five times higher as is the case of Zone 1. It appears to be a reduction in the variance between zones and a tendency to a homogenization at the regional level except for Zone 4, where, despite being the area most affected by wildfires, burned surface does not increase five-fold in the pessimistic scenario. Concerning seasonal variations (Figure 6), while at the moment risk period generally lasts from March to September with peaks in June (Zones 3-4) or July (Zones 1-2), predicted wildfires in long-term period are mostly confined in the summer season, especially in June and July months, which means that the high-risk period is shortened but total burned surface significantly increases, especially in Zone 1. In fact, big wildfires in autumn and winter are limited to their proximity with spring or summer and are rarely to happen. Zone 4 moves its maximum from June to July, consistent with the rest of the region. The patterns for RCP4.5 and RCP8.5 are rather similar with more pronounced changes in RCP4.5 during the year.