# Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen
## 1 Introduction
Several modeling techniques have been used to model flood hazards and predict their occurrence in literature reviews, such as [PERSON] and [PERSON] (2009) modeled watercourses and predicted floods in Accra, Ghana, using a vector-oriented GIS and a digital elevation model (DEM) [16]. At present, techniques for order preference by similarity to an ideal solution (TOPSIS) [17], analytic network process (ANP) [18], analytic hierarchy process (AHP) [18], and logistic regression [19] are examples of multivariate or multi-criteria decisions making (MCDM) methods utilized in flood hazard assessments [20]. The MCDM methodologiesare regarded as subjective but straightforward [21]. Physically-based modeling systems can describe the specifics of a flash flood, but they require a large amount of input data and significant processing resources [22]. Compared to the MDCM and physically-based simulation approaches, the statistical and machine learning methods transcend the MDCM and physical simulation methods' drawbacks and can rapidly and accurately predict the flooding susceptibility [13; 23; 24; 25; 26]. The frequency ratio (FR) [27], weights of evidence [28], index of entropy [29], and statistical index [7] are the main statistical tools employed in flood susceptibility mapping. However, because of the complex mechanism by which floods occur, the accuracy of these statistical approaches is limited [30]. Therefore, various ML methods are being used to solve the problems mentioned, e.g., logistic regression (LR) [19], support vector machine (SVM) [31], artificial neural network (ANN) [32], random forest (RF) [33], Extreme gradient boosting (XGBoost) [34], decision tress (DT) [5], Naive Bayes (NB) [35]. At present, deep learning models, e.g., convolutional neural network (CNN), recurrent neural network (RNN) [36], deep neural network (DNN) [37], are widely used in disaster prediction, which shows higher accuracy in predicting flash flood and landslides, but the drawback in using DL models is a large dataset, which is always a problem in data-scarce regions like Yemen. Several studies have demonstrated that ensemble machine learning models can make accurate predictions of natural hazard susceptibility, such as flood hazards [30; 38; 39; 40; 41], air pollution [42], droughts [43], gully erosion [44; 45], land subsidence [46], landslides [47; 48], groundwater [49], and earthquakes [50]. [PERSON] et al. [51] used various machine learning models, namely LR, KNN, AD-boost, and XGBoost for flood risk management in Hyderabad (India), and found XGBoost to be the more accurate model in delineating flood-prone areas with the AUC score of 0.83. [PERSON] et al. [33] assessed flash flood susceptibility by using RF, Xgboost, CART, and BRT in which RF achieved the highest accuracy with AUC values of (0.956) followed by BRT with AUC values of (0.899), Xgboost (0.892), and CART with AUC values of (0.868). These approaches are founded on considering past flood events as dependent variables and flood conditioning factors as independent variables. Additionally, past flood data may be utilized to assess the performance of these models, which is one of their advantages. However, no universal model has been demonstrated to be superior in all areas of research [38]. Tarim city, Yemen, is prone to flash floods and experiences several episodes of flash floods during the rainy season almost yearly, resulting in the loss of lives, properties, and ecological environment [52]. However, no effort has been made to map flood susceptibility. Therefore, this study aims to delineate flood-prone locations and determine the causative factors of flood occurrences in Tarim city. The novelty of this study is that the advanced machine learning methods, namely KNN, XGBoost, RF, and NB algorithms in Tarim, Yemen, are used to assess flash flood susceptibility based on twelve flash flood conditioning factors. In addition, RS data (Sentinel-1 images) were used to detect flooded areas in the study area.