## 4 Discussion  
The behavior of wildfires in the studied region is very sensitive to burned surface area. The patterns of fires are strongly linked to meteorological variables depending on burned surface area. The k-means algorithm is capable of establishing the thresholds of all factors taken into account and differentiates the clusters in which fires in each zone can be divided. Wildfires with burned surfaces larger than around 30,000 m\({}^{2}\) account for more than 80% of the total burned area in the dataset period (2000-2014) and appear to be in all zones as the threshold that makes the difference between small and big fires. Small fire clusters do not follow a clearly identifiable pattern with used techniques, but the ANN method proved satisfactory results in wildfires with burned surfaces greater than 30,000 m\({}^{2}\). Analysis of climatic, ecological, and geographical variables carried out by an ANN reject geographical and ecological factors' influence in big fires, possibly due to little change in ecosystems in the four zones, albeit local variations of forests of pine alternated with areas covered in scrub, bush, and grassland in proportions greater than 20%, especially in coastal areas. Furthermore, an extension of 11,314 km\({}^{2}\) with geographic coordinates in a range of 1\({}^{\circ}\) of latitude (between 38\({}^{\circ}\)45\({}^{\prime}\)N and 37\({}^{\circ}\)23\({}^{\prime}\)N) and less than 2\({}^{\circ}\) of longitude (between 0\({}^{\circ}\)41\({}^{\prime}\)W and 2\({}^{\circ}\)21\({}^{\prime}\)W) and a topography whose valleys and plains account for 75% of the total region has a reduced impact on burned surface. Therefore, the variables finally used in this study were precipitation, relative humidity, wind speed, temperature, and the weather fire indices FWI and AI, though the latter, in the ANN classification, only influences Zone 4 as it had already been reflected in the previous study of [PERSON] et al. (2008), owing to higher yearly maximum temperatures in southern areas. Increase in precipitation around 20% in both climate change scenarios will not by itself reduce the total burned surface because a rising temperature of 3 \({}^{\circ}\)C, on average, cannot be compensated with regard to ignition and fire spreading. Furthermore, wind velocity proves to be a relevant factor as it was suggested by [PERSON] and [PERSON] (2009). An increase of 15-25% in wind speed, apart from increasing temperature, leads to double the number of fires per year in all zones and even to triple in Zone 4, where the increase of temperature is maximum, around 4 \({}^{\circ}\)C in the RCP8.5 scenario. The performance of ANNs in classifying the size of fires was assessed using different partitions in training-test datasets (from 50-50% to 90-10% in training-test, respectively). Furthermore, various evaluation criteria were also used, providing more robustness and accuracy to results obtained. The higher the training rate is, the better are the results achieved for all measures of evaluation, until the test partition percentage decreases below 20%, though accuracy even exceeds 80% in Zones 2 and 3 at the expense of greater variance. ANN classification showed good sensitivity, specificity, and accuracy