## 3 Materials and Methods
A fault density derivative dataset was developed with the ArcGIS \"Line Density\" tool and the active faults from the GEM Global Active Faults database [33], utilizing a search radius of 250 km and an output cell size of 25 sq km. The new dataset represents the fault length (km) per square kilometers of area. Maximum Entropy (MaxEnt), a \"presence-only\" machine learning algorithm [34], is used due to the ambiguity of \"absence\" in this context and the dependence on landslide inventories that were not collected through manual field surveys and thus without verified locations. Absence does not necessarily mean that there are or were no debris flows in an area. It means we do not know and/or we do not have substantiating data sources or ability to conduct field surveys, particularly at a continental scale. MaxEnt utilizes the environmental data across the entire study area, rather than the area strictly associated with the presence data, which is an important factor when working with limited presence data [35]. The MaxEnt model output is a maximum likelihood estimate of relative probability of presence. MaxEnt is a widely used technique in biological species distribution modeling with recent and growing interest in its use for landslide susceptibility modeling due to its predictive success compared with other methodologies in \"presence\" only scenarios [27; 36; 37; 38; 39; 40]. The MaxEnt model renders information for those debris flow predisposing factors that provide the greatest contribution to the susceptibility analysis. For this study, each MaxEnt model was run with five replications (cross-validation), logistic output format, with jackknife tests of variable importance. The MaxEnt models were developed with the event point data. Since MaxEnt requires the event data input be in point format, it alone does not lend itself to assessing the impact using debris flow locational accuracy buffered areas and associated 'what if' scenario representations. Therefore, in addition to MaxEnt software v. 3.4.4 [41] for susceptibility and environmental factor analyses, ArcGIS Pro 2.7 [42] was used for creating uncertainty buffers around each point based on its location accuracy (uncertainty), spatial calculations (pixel math for determining differences between models), statistical analyses (standard deviation, mean), and additional scenario representations. All maps presented herein include base maps from ArcGIS(r) and are the intellectual property of Esri and are used herein under license. Five models were developed with MaxEnt. The first model was developed using the original inventory of 99 Europe debris flow events with varying locational uncertainties. This model will subsequently be referred to as \"Original\". The second model utilizes a training dataset created from 93 randomly generated points, randomly located within each of the original 93 locational uncertainty buffers. In ArcGIS, for each event buffer (regardless of location accuracy), the \"Create Random Points\" tool was used to generate a new point randomly located within each buffer. These new points were then used in MaxEnt to create a susceptibility model. This model is referred to as \"93 Random\".