# Physical Vulnerability and Landslide Risk Assessment in Tegucigalpa City, Honduras
## 4 Discussion
As part of the risk modeling design, various techniques have been proposed to address these uncertainties. The random uncertainty associated with the intensity of the hazard and the response of infrastructures and the epistemological uncertainty linked to the generalization of a limited number of home damage surveys to all homes in the study area are common issues faced when using probabilistic disaster risk models for small areas such as cities or watersheds. In these models, vulnerability functions are built with a limited amount of empirical data or limited results from physical models. Due to the limited availability of data, it is not possible for these data to be statistically representative. To handle this source of uncertainty, the data are used to parameterize a statistical function that characterizes vulnerability and damage as a random variable, in a similar way to the method used in this study. Regarding the epistemological uncertainty related to the use of a limited number of soil trial data to characterize the mechanical properties in the study area, probabilistic techniques were applied to manage the uncertainty associated with these data. Additionally, the soil mapping was a key input in the reduction in uncertainty associated with the mechanical properties, as it contributes to the definition of homogenous units. In a previous study, modeling was conducted on some pilot landslides to reduce the epistemological uncertainty related to the suitability of the infinite slope model to represent the landslides in the study area. This analysis revealed that the model has biases, particularly underestimating the landslide extension in low-slope areas. This bias was addressed by applying geomorphological methods to incorporate these low-slope areas into areas characterized by high landslide probability. Validation of a probable loss estimate is complex, as it represents future values. In this context, the use of historical data for probabilistic risk validation has some limitations [42]. One of the main limitations is that probabilistic modeling represents future losses, which can exceed historic losses. Furthermore, in the study area, there is no information on historical losses attributed to landslides. The DesInventar database does not include loss information, and the existing national damage assessments do not discriminate losses associated with landslides. In a previous study, the significance of the colluvium and residual soil layer in explaining the spatial distribution of the landslide inventory was validated, obtaining an AUC of 88.64%. However, the subject of the present study is the estimation of probable losses and, so, this is the indicator that should be validated. One option to estimate the accuracy of the results could be to compare them with the probable losses obtained for the same study area using a different method [42]. However, to date, the present study is the only one providing an estimate of landslide risk made in the MCD. On the other hand, there are no widely accepted methods for landslide risk estimation that can be considered as a standard to validate the results of the present study [14; 15; 16]. A flood risk assessment was carried out in 2015 for the same study area [21]. As the estimation of flood risk is based on reliable and widely accepted methods [53], the comparison of the results provides a benchmark for validating the findings of the present study. The results of the flood risk assessment were projected for 2020, obtaining an AAL of USD 6.34 million, with an exposed population of 92,531 people. The risk expressed as AAL was similar for both landslides and floods (USD 7.26 million and USD 6.34 million, respectively), although it was 14.5% higher for landslides. This difference in AAL can be attributed to the higher exposed population for landslides, which is 77% greater than that of floods.