## 3 Materials and Methods  
We developed a model of the risk of ignition of forest fires in the Maule region through a machine learning model. For this, we defined the ignition of forest fires as a dependent variable and selected a set of independent variables as possible predictors of ignition. The construction of the database was in two stages. First, spatially represented points of the ignition variable (binary variable) were generated. Then, the values of the set of independent variables were estimated for each of these points. The dependent variable was generated from the Corporation Nacional Forestal (CONAF) data, published as official data on its website (CONAF [[https://www.conaf.cl/incendios-forestales/incerdios-forestales-en-chile/estadisticas-historics/](https://www.conaf.cl/incendios-forestales/incerdios-forestales-en-chile/estadisticas-historics/)]([https://www.conaf.cl/incendios-forestales/incerdios-forestales-en-chile/estadisticas-historics/](https://www.conaf.cl/incendios-forestales/incerdios-forestales-en-chile/estadisticas-historics/)) (accessed on 20 May 2022)). From this, we obtained information on the coordinates, start date, control, cause, and magnitude, of the fires that occurred in the Maule region between 2013 and 2015. The period defined for collecting information was due to the need to have a rich source of available data on fire ignition and independent variables before the megafire of the 2016-2017 season. On the other hand, we decided to generate the model with data before the megafire to contrast it with what happened during that event to validate its results. With this, we looked for a model that allowed us to explain the ex-post distribution of the ignition points of forest fires, while predicting the risk of ignition in the future. We assigned the value 1 (one) to the points where a fire occurred in the indicated years and a value 0 (zero) to the points where there was no ignition. The assigning points that represented areas with no ignition of wildland fires were carried out randomly. Points with the value 0 were assigned to located areas more than 500 m from an ignition point. For the construction of georeferenced data, we used the geographic information system QGIS (version 3.20.3. for Windows/Copyright (c) 2000, 2001, 2002, 2007, 2008 Free Software Foundation, Inc. <[[http://fsf.org/](http://fsf.org/)]([http://fsf.org/](http://fsf.org/))>). We obtained 3784 points, of which 1892 indicate the ignition of forest fires between 2013 and 2015 in the Maule region; these correspond to the red points in Figure 2. The 1892 blue points generated by the model represent areas with no ignition in the same period. The independent variables selected for the construction of the model were based on the work of [PERSON] et al. [40]. According to the authors, the scientific evidence indicates that this set of variables is the one that best represents the territorial characteristics of those places where wildland fires occur in interface areas. Based on the results of this group of researchers, we worked with 14 independent variables organized into the following 3 classes: (a) human activity, (b) geography and topography, and (c) land cover (Table 1). Each variable was spatially represented in a 30-m resolution raster. To assign the values of the independent variables at each point, we defined a zone of influence of 500 m (centered at the point of ignition). These buffers were built by taking a circumference of a radius of 500 m around each point of the raster. Each one was assigned the value (percentages, averages) of each explanatory variable to avoid bias. Each of the 3784 points was assigned values for these 14 variables. Thus, the model was trained with the values obtained between 2013 and 2015. We seek to identify the variables that most influenced the fire's start in the past. With this, we estimate what factors generate a greater probability of a fire. Based on the methodology used by [PERSON] [40], we use the bagged decision tree, BDT model [75].