## 5 Discussion and Conclusions
For this reason, it is imperative to consider the variables above in wildland fire risk management, especially given the uncertainty that a new megafire might occur in the region. The creation of models that make it possible to understand and make visible the risk of wildland fire ignition is necessary but not sufficient. If disaster risk is still understood only as the future projection of damage, these models only serve to identify the risk, not to manage it. Forest fire management requires integrating risk as a decision. This meansimproving the policies that regulate the density of forest plantations, the maintenance and expansion of firebreaks in interface zones, land use, and post-fire cleaning of areas where tree species' seeds are irregularly disseminated. It also means integrating the ignition risk into the measures to repair the damage caused by wildfires. Developing ignition models, such as the one in this work, is undoubtedly necessary to manage wildland fires. An example is the verification of sectors with a greater probability of ignition that, according to the model, are strongly associated with the areas where multiple fires occurred in 2017. This information will allow fire governance actors to implement better strategies to prevent wildland fire ignition. Nevertheless, we must be aware that, even so, forest fires will continue to occur. The risk of ignition is higher in regions such as the study area, where climate change has strongly impacted territory characteristics and conditions. For this reason, integrating the ignition risk into decisions also means implementing campaigns and actions to improve the ability to react to this kind of phenomenon. It should be noted that the model developed in this work accounts for the particularities of the study area. The model was trained with historical data from the Maule region in Chile. Therefore, the results obtained are valid only for the case analyzed. However, working with the bagged decision tree model has the following significant advantage: it is possible to replicate it in other areas with different characteristics. It is possible because the model learns from the data provided to it. In addition, variables such as human activity, geography, topography, and land cover can be obtained for other areas of interest. The only limitation is that these layers of information, particularly land cover, are scarce in many countries. One of the challenges that arise from this work is to continue deepening the characterization of the ignition risk. We believe that obtaining better views of the interface areas and the factors associated with risk is necessary. This will allow professionals and people to integrate this information. Visually identifying the components and characteristics of the territories where the ignition risk is higher can contribute to a better understanding of how to prevent and prepare for wildland fires. Conceptualization, G.A.d.l.C. ; methodology, G.A.d.l.C., G.A., R.C. and C.A. ; software, G.A., R.C. and C.A. ; validation, G.A and R.C. ; formal analysis, G.A.d.l.C., G.A. and R.C; investigation, G.A.d.l.C., C.A. and P.O. ; resources, G.A.d.l.C., G.A., C.A. and P.O. ; data curation, G.A and C.A. ; writing--original draft preparation, G.A.d.l.C. ; writing--review and editing, G.A.d.l.C. ; visualization, C.A. ; supervision, G.A.d.l.C. ; project administration, G.A.d.l.C. ; funding acquisition, G.A.d.l.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Agency of Research and Development of Chile, ANID, Fondecyt N\({}^{\circ}\) 11190483. Not applicable.