# Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen
## 3 Results
### Variable's Importance  
The relevance of each conditioning factor for the four models was assessed using R software. The results indicate that the slope is the most critical factor for all used algorithms, followed by the drainage density, TWI, and elevation (Figure 9). These findings support earlier research revealing that these factors are essential in the flood susceptibility [22; 104]. Further, the other factors show low and various variable importance, whereas the factors (rain duration and aspect) show the least essential variables. It is worth noting that flooding is associated with rainfall; however, in our study, rainfall duration was the least important  
Figure 7: The flowchart adopted for this study. factor after aspect contributing to flooding susceptibility because flash floods are sudden and caused by sudden heavy rainfall in a short period of time. Elevated areas typically receive more rainfall, which flows downward and inundates areas with gentle slopes and low elevated areas. Our findings are consistent with previous studies where rainfall is the least important contribution to flood susceptibility [27; 105; 106; 107]. Figure 8: Flood detection using Sentinel-1 data in Tarim city. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Factors**} & \multicolumn{2}{c}{**Collinearity Statistics**} \\ \cline{3-3}  & & **Tolerance** \\ \hline
1 & Aspect & 0.916 \\
2 & Curvature & 0.905 \\
3 & Drainage density & 0.514 \\
4 & Elevation & 0.393 \\
5 & LANDUSE & 0.775 \\
6 & NDVI & 0.726 \\
7 & Rain duration & 0.565 \\
8 & Rain intensity & 0.592 \\
9 & Slope & 0.292 \\
10 & SOIL & 0.497 \\
11 & SPI & 0.871 \\
12 & TWI & 0.369 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Multicollinearity analysis of the flood conditioning factors. In summary, there is a similarity in the significance rankings of RF, KNN, NB, and XGBoost variables may be seen (Figure 9), and factor distribution by floods occurrences (Figure 10)  
The relevance of each conditioning factor for the four used models was assessed using R software. The variables in the RF model with the highest importance were found to be slope and drainage density, while variables with medium to lesser importance included TWI, elevation, SPI, curvature, and NDVI. In the case of soil, land use, rain intensity, rain duration, and aspect, no such importance was discovered in the modeling of flood susceptibility. The variables with the highest importance in the KNN model are slope, TWI, drainage density, elevation, SPI, and soil, while those with medium to low importance are land use, NDVI, and curvature. There is no such importance in flood susceptibility modeling for factors such as rain duration, rain intensity, and aspect. The highest importance of the variables in the NB model was found in the slope variable, TWI, drainage density, elevation, SPI, and soil, while the other variables, such as land use, NDVI, and curvature had a medium to less importance. There was no importance found in flood susceptibility modeling for rain duration, rain intensity, and aspect. The only variable with the highest importance in the XGBoost model is the slope, while those with medium to less importance are drainage density and elevation. There is no such importance in flood susceptibility modeling for curvature, NDVI, TWI, SPI, soil, and use, rain intensity, rain duration and aspect. Flooding probability was calculated independently by investigating the link between every explaining variable and flash flooding frequency. Using histogram analysis, a flood factor distribution (FD) was produced for each of the variables, with each variable being separated into different classes. The distribution of flood incidence among the various classes of each variable was evaluated using FD analysis. The FD analysis was carried out (Figure 10). The majority of flood incidents occurred in low-lying areas, as well as locations with high accumulate water (TWI) and area of low SPI.