## 6 Conclusions  
The central hypothesis of this study is the principle that locational uncertainties may be present in any landslide inventory whose data is gathered by means other than direct field survey or detailed remote sensing identification tools with a known significance level of accuracy and ground control. This study confirms that the existence of locational uncertainties has a significant impact on the resulting debris flow susceptibility analyses. It is imperative that landslide inventories include a location accuracy (uncertainty) attribute, as is true in the NASA inventory used for this study. How to approach a landslide analysis based on data without an exact known location or measure of uncertainty is beyond the scope of this study, but one which warrants further research. In this study, a MaxEnt debris flow susceptibility model was developed utilizing the full inventory of data that has varying levels of location uncertainty. The resulting predisposing factors with the highest contribution to the model (precipitation, fault density, soil class), were investigated to identify and demonstrate the scope of impact of location uncertainty on factor class determinations and ultimately on debris flow susceptibility classification. The resulting susceptibility map demonstrated the significance of the problem of multiple susceptibility classifications within the buffered areas of uncertainty, whereby it is not possible to determine a single susceptibility for a given debris flow event, beyond those with a known \"exact\" location. In search for an alternative to using the full inventory, additional models were developed based on subsets of event data with differing location uncertainties. When uncertainty is known, it is suggested to share this information with the end user in a manner that will facilitate the use of the model for subsequent more detailed analyses. \"It is critical to identify, assess, and quantify uncertainty in spatial data and analysis because not accounting for uncertainty can lead to overconfidence in the conclusions\" [45]. The validity and limitations of a spatial model are best understood graphically yet representing uncertainty \"remains a persistent and relevant challenge\" in the spatial information sciences \"particularly as it applies to decision making and analysis\" [46]. Therefore, a novel visual representation was developed, which allows model users to identify and use susceptibility areas of least uncertainty impact, according to their intended use. Additional sources of data uncertainty may be inherent in other attributes of the inventory, as well as uncertainties associated both with the spatial assignment and attributes of the environmental factors, and the modeling processes. This research exclusively demonstrates the importance of event location accuracy and the impact of its uncertainty. Location uncertainty is not the only uncertainty associated with landslide susceptibility modeling but it is a fundamental one. To adequately highlight the impact, it is treated singularly, ignoring other potential uncertainty variables. Modeling all possible uncertainties is outside the scope of this study, and we believe it is unlikely that additional uncertainties would lessen the impact of locational uncertainty. The cause, nature, and handling of mapping errors (uncertainties) has been the subject of extensive research [17; 18; 19; 47; 48; 49; 50]. Uncertainty and uncertainty handling is context dependent. The combination of uncertainties in data, analytical methods, and the overlay processes, may be additive, multiplicative, and non-linear [51]. If a susceptibility map, at any scale, is to be used to direct further research or mitigation efforts, a confidence level associated with the results is desirable [52]. This confidence will be dependent upon many facets of the model, one of which is the reliability of the location of the events used to train the model.