# Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen
## 4 Discussion  
The first and most critical stage in flood risk assessment is determining how sensitive an area is to floods through flood susceptibility mapping. Flood-prone areas can be identified, and the necessary support solutions can be put in place to reduce flood-related losses. In this study, we used various geospatial datasets integrated with machine learning and geographic information systems to investigate and analyze flood susceptibility in the data-scarce region. Due to the annual monsoonal rains, Yemen is prone to flooding. Flooding is a natural occurrence that cannot be avoided entirely, causing significant economic losses and infrastructure and natural ecosystem damage [1]. Commonly, climate change has been found to impact flood occurrences substantially. It is still unclear how climate change may affect floods in the future, notably the seasonal effects of climatic factors, which need more investigation. However, a poor understanding of flood management might emerge from a lack of information about the spatial variability of floods. Flood impact reduction can be achieved by determining the primary factors influencing flood events and producing a flood susceptibility map. Nevertheless, numerous other hydrological, geological, topographical, and morphological factors influence floods [108]. Furthermore, only some of these factors are included in flood susceptibility models; hence, choosing appropriate flood-affecting factors is a critical step in flood susceptibility modeling. Therefore, for reliable flood susceptibility maps, the research area must be affected by understood factors. No systematic research has been done on flood events in Tarim city (Yemen). This study tries to fill this knowledge gap by comparing four ML algorithms to find the most effective one for predicting FFS in a semi-arid area. In America, Europe, and Asia, similar research has been conducted for flood susceptibility maps. The performance \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **RF** & **KNN** & **NB** & **XGBoost** \\ \hline Positive predictive value (\%) & 0.9494 & 0.8202 & 0.7308 & 0.9487 \\ Negative predictive value (\%) & 0.9437 & 0.9016 & 0.9348 & 0.9306 \\ Sensitivity (\%) & 0.9494 & 0.9241 & 0.962 & 0.9367 \\ Specificity (\%) & 0.9437 & 0.7746 & 0.6056 & 0.9437 \\ Accuracy (\%) & 0.9467 & 0.8533 & 0.7933 & 0.9402 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model performance in this study. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **AUC** & **95\% of Confidence Interval (CI)** & **Kappa Index** \\ \hline RF & 0.982 & 0.8976-0.9767 & 0.893 \\ KNN & 0.928 & 0.7864-0.9057 & 0.7037 \\ NB & 0.920 & 0.7197–0.8551 & 0.578 \\ XGBoost & 0.979 & 0.8892–0.9722 & 0.8797 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: AUC and Kappa index for the models. of susceptibility modeling utilizing different suitable ML algorithms has been the focus of several studies in this field. Commonly, techniques based on ML and artificial intelligence (AI) save time and money and can provide a high degree of accuracy. Based on results of variable importance, the most important factor that may cause flooding in this study is the slope, followed by drainage density, TWI, and elevation. These findings are in agreement with results from other recent studies [109; 110], which stated that slope is the most important factor in flood occurrence [111; 112]. Drainage density is a fundamental feature of river basins that represents relief, flood peak, and geology from a hydrological standpoint [113]. Floodwaters usually inundate areas with flat slopes at low elevations. Increased rainfall at higher elevation zones is less prone to flooding because the water flows from high elevation zone to low-lying areas, and therefore elevated areas are less prone to flooding [27]. Additionally, the areas of high TWI have saturated soil, so the flood potential increases as TWI increases as the soils cannot absorb more water resulting in flooding [39; 114].