# Physical Vulnerability and Landslide Risk Assessment in Tegucigalpa City, Honduras
## 4 Discussion  
While probabilistic models have been widely utilized for disaster risk estimation, their use requires making several assumptions and managing uncertainties regarding \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**LI 1** & **LS 1** & **VI 1** & **LI 2** & **LS 2** & **P 2** & **AAL 1** \\ \hline
0.78 & 2.1 & 1.3 & 1 & 0.77 & 0.23 & 0.23 \\
2.1 & 6.6 & 3.7 & 0.77 & 0.5 & 0.27 & 1.01 \\
6.6 & 9.6 & 7.9 & 0.5 & 0.25 & 0.25 & 1.99 \\
9.6 & 14.6 & 11.8 & 0.25 & 0.16 & 0.08 & 0.99 \\
14.6 & 21.3 & 17.6 & 0.16 & 0.008 & 0.16 & 2.79 \\
21.3 & n/a & 21.3 & 0.008 & 0 & 0.008 & 0.17 \\  & & & & & TOTAL AAL 1 & 7.26 \\ \hline \hline \end{tabular} \({}^{\dagger}\) USD, 2 probability values.  
\end{table} 
Table 6: Summary of the values used to estimate the average annual loss (AAL) using the interval method. LI: lower limit. LS: upper limit. VI: interval value. Q: probability. Figure 10: Landslide loss exceedance curve for the study area. Figure 9: Probable loss values in USD at the pixel level, for a return period of 125 years. the hazards, the homogeneity of the exposed assets, and their vulnerability. For slow-moving landslides, there is a lack of vulnerability data on exposed assets and difficulty in estimating the intensity of landslides and their temporal probability, making disaster risk studies associated to these hazards particularly scarce. However, a conceptual model was developed in this study to analyze the disaster risk associated with slow landslides, which overcomes many of the identified limitations. Additionally, using a physical estimation model for spatial probability estimation provides significant advantages over statistical models. Physical models do not rely on the principle of actualism, allowing for the identification of potentially unstable areas which may not be revealed by a statistical model. They also do not depend on an inventory of slope movements for development, contributing to the independence of the temporal and spatial probabilities. Moreover, using a physical model helps to overcome one of the main limitations of landslide susceptibility statistical models: the use of variables that are widely available but may not have the highest explanatory capacity [35]. The main assumptions made for this model included the following: (i) the soils were completely saturated; (ii) the data of the events recorded in the press represent the events that occurred in the study area; and (iii) the soil parameters follow a normal distribution. Assuming that the soils were completely saturated, it is important to achieve independence between temporal and spatial probability. This means that the spatial probability model should not include variables that change over time. It is reasonable to assume that, for this movement to occur, there must be a significant level of saturation in the soil, as it is the main triggering factor. However, the assumption of total saturation could lead to an overestimation of spatial probability. On the other hand, the data used to estimate temporal probability may contribute to an underestimation. Databases based on press reports, such as the one in this case, have a bias toward slope movements that caused damage and do not include other events that might have occurred in sparsely populated areas [34]. In relation to the assumption of the FOSM method that the different variables follow a normal distribution, this is always assumed when applying this probabilistic technique. The primary sources of uncertainty for estimating the risk of slow-moving disasters include (i) random uncertainty related to the intensity of movements and the response of the house, as well as epistemological uncertainty due to the limited amount of data available to characterize the potential damage that landslides could cause on dwellings across the study area, and (ii) epistemological uncertainty due to the lack of knowledge regarding the input parameters for the Infinite Slope model and its applicability to the characterization of landslides in the study area.