# Estimating Flood Inundation in Urban Areas Using a Scenario Generation Method and Inundation Graphs
## 4 Discussion  
Another possible way to apply this developed scenario method is by targeting the simulation results to specific areas in the site shown in Figure 9. This would be necessary if the target region included structures of interest; the sewage plant's settling pond, reactor, and chamber were among the buildings that made up the application's present target site. The various flood scenarios can demonstrate each urban building's vulnerability and be used for additional assessment. The graph also shows the difference in results when using different distribution functions up to 11 percent of estimated depth, which depicts the importance of using various scenarios for extreme events such as the return period of 500 years. Later, these results could lead to Intensity-Duration-Frequency (IDF) curve creation and further analysis of the difference between the extreme value distributions using other theoretical distributions such as Log-Pearson III. In the analysis of the developed scenario generation method, a flood hazard rating index was used to show what scenario results would be considered dangerous to individuals [36; 37]. The index is shown below:  
\[HR(i)=h(i)\times\{v(i)+0.5\} \tag{9}\]  
where \(HR\) is the flood hazard rating index at point \(i\), \(h(i)\) is the water depth at point \(i\), and \(v(i)\) is the velocity magnitude of the water at point \(i\). The utilized application flow model is a depth-averaged model, and the flow velocity is two-dimensional so there is a vector for the x-direction and y-direction. The velocity magnitude will then be the square root of the sum of the squares of velocities in the x and y directions. The result of the hazard rating is shown in Figure 10, with the scenario return period for the y-axis and time used in Figure 8 for the unsteady simulation as the x-axis. The time used is the same time for the unsteady simulation that is depicted in Figure 8 in a scenario, which uses input from the estimated flow rate in Figure 4. As water depths are calculated during the time span, the flow velocity is also calculated and used in creating the hazard matrix. If the HR is less than 0.75, floodwater would cause minimal damage to individuals; if it is more than 2.5, there is a high risk of damage [36; 37]. The safety in flood situations would be calculated using these thresholds of 2.5 and 0.75 for classification. The results were shown in a matrix form for each building, to find the values of the flood hazard rating index for scenarios easily. The created matrix is shown below with application from the scenarios. Buildings A and B which are the settling pond and reactors showed high flood hazard rating values in certain scenarios, while building C showed low values, which would be considered relatively safe. Figure 9: Results of scenario generation method to specific buildings. In regard to the hazard rating matrix, the formulation of a flood danger matrix and the corresponding hazard rating are generally based on expert judgment and customized to a particular case study such as this research. Various classifications of flooding hazards exist, and there is a lack of standardization on an international scale. Standardizing the assessment of flooding hazards is another issue on this topic, so this research decided to continue selecting a previous version which is simple and physically grounded based on water depth and velocity. Overall, the main points of this paper are the following:  
1. This study created a scenario generation algorithm based on return periods and extreme value distributions using a two-dimensional flood model with the inundation method storing database according to various flood scenarios;
2. This study provides a tool that could analyze the potential impact of different flood events using a pre-stored inundation database;
3.