# Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen
## 4 Discussion
The AUC values of all four models were greater than 0.90 in terms of ROC results, indicating that the four models performed well in predicting flash flood susceptibility. Besides that, the outcomes of other statistical metrics like kappa index, sensitivity, specificity, and accuracy, revealed that all models produced good and reasonable results. In our study, in terms of performance, the tree-based ensemble algorithms RF and XGBoost outperform other ML algorithms, where the RF algorithm provides robust performance (AUC = 0.982) for assessing flood-prone areas with only a few adjustments required prior to training the model. These findings are consistent with previous research, which has shown that tree-based ensemble algorithms perform better than other algorithms [33; 51]. According to a study [51] for flood susceptibility assessment in Musi River, Hyderabad, India using ML models, RF and XGBoost outperform other ML algorithms, logistic regression, support vector machine, K-nearest neighbor, adaptive boosting (AdaBoost), which are compatible with the results of this research. [PERSON] et al. [115], in the study Mapping flood susceptibility in mountainous areas on a national scale in China, by using the RF model, stated that the RF model could identify the flood susceptibility with satisfactory accuracy. [PERSON] et al. [116] in modeling flood susceptibility using data-driven approaches of naive Bayes tree, alternating decision tree, and random forest methods stated that the RF method is an efficient and reliable model in flood susceptibility assessment. [PERSON] et al. [33] assessed flood susceptibility by using RF, XGBoost, and boosted regression trees and stated that RF was the most accurate in predicting flash flood susceptibility. That study's outcome was in line with what we found in our research. Although it had the lowest predicted accuracy of the three ways evaluated, the NB method was similarly beneficial. As a result, based on their performance and ease of interpretation, this study shows that the chosen models are genuinely possible. On the other hand, one of the drawbacks of the study was the lack of critical hydrological data, such as flood depth, velocity, and discharge, making developing a robust model difficult. Flood modeling is a complicated operation fraught with uncertainties. As long as credible historical flood inventory maps are available, machine learning algorithms can efficiently address these uncertainties [117]. So, to avoid the uncertainties, we made a flood inventory map of the study area by using flood damage reports and Google earth pro and field visits for historical floods from 1996, 2008, and 2021 and verified the flood inventory by flood sentinel-1 SAR data for flood episode 2021 [38]. All the spatial data resembled 12.5 m resolution to avoid uncertainties arising from inconsistent spatial data resolution. As long as credible historical flood inventory maps are available, machine learning algorithms can efficiently address these uncertainties [38]. The proposed models could be a valuable and novel strategy for managing flood threats in dry and semi-arid regions like Tarim (Yemen). However, as with any other study, the results of the current study are susceptible to error and uncertainty due to factors such as subjective classification of flood-influencing factors, selection of performance indicators, training, and testing datasets. Each of these factors necessitates more research to demonstrate how these uncertainties influence the final flood susceptibility maps and subsequent decisions. Future research should consider the effect of these uncertainties by choosing other flood factors, such as daily or sub-daily rainfall, classifying the flood factors in collaboration with stakeholders [117; 118], conducting a sensitivity analysis of the effect of classification of the observed dataset (other than 75% and 25% for training and testing), and evaluating the efficacy of the four methods using alternative goodness-of-fit measures [117; 119].