# Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen
## 5 Conclusions  
Flooding is a natural disaster that threatens people's lives, and the structural integrity of buildings in flood-affected communities can never be avoided entirely. Because of this, it is very important to improve flood forecasting and prevention methods to reduce the number of people who die and the negative social and economic effects of floods.  
RS data (Sentinel-1 images) were used to detect flooded areas in the study area. Used the Sentinel application platform (SNAP 7.0) for Sentinel-1 image analysis and detecting flood zones in the study locations. Flood spots were discovered and verified using Google Earth images, Landsat images, and press sources to create a flood inventory map of flooded areas in the study area. This study used four ML algorithms (RF, KNN, NB, and XGBoost). The models were built using a spatial database that comprised 12 topographic and geo-environmental flood conditioning factors and data from 300 previous flooding occurrences. The tests revealed no evidence of multicollinearity between the identified conditioning factors. The validation findings revealed that all of the models utilized performed admirably, with the RF and XGBoost models outperforming the others. However, RF is more computationally efficient than XGBoost since training the model with RF requires less execution time. Thus, the RF model might create a flood susceptibility map and a potential method for flash flood prediction in the era of big data due to its capacity to handle multiple types of variables and represent complex non-linear interactions.  
In addition, we discovered that the KNN model provided us with a false alarm area in some locations, even though there was no flood in the actual observations, but there was a flood predicted by the model. These locations were on higher ground, and it was not possible for floods to occur there.  
The results show that approximately 4.53% to 13.34% of the overall area is highly vulnerable to floods. The resultant map may serve as a basis for establishing plans for minimizing flood susceptibility and assisting in developing adaptation measures. The difficulty in obtaining relevant intense precipitation records and combining them with the results of flood simulation models is a limitation of this study. Moreover, several factors are pertinent to flood occurrences, such as flood depth and velocity parameters, but we couldn't acquire them. These data could be used in future work to make it more robust.  
[PERSON] and [PERSON] performed the research, modified the codes, analyzed the data, and wrote the manuscript. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] designed the research and extensively updated the manuscript. [PERSON] proposed flood susceptibility mechanism and conceptual framework, revised, and agreed to the published version of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Science and Technology Development Planning of Jilin Province (Grant No. 20200403074 SF) and the Major Scientific and Technological Program of Jilin Province (Grant No. 20200503002 SF).  
The data that support the results of this research are available upon reasonable request from the author.  
We express our gratitude to [PERSON], who contributes to model design and the Scientists Adoption Academy (scadacademy.com, accessed on 1 December 2021) online research collaboration website for facilitating the research development interactions.