# Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen
## 2 Methods and Data
### Model Validation  
The validation and accuracy assessment in modeling is critical. A machine learning model should preferably not be assessed on the same data on which it was trained, as this can cause the model to overfit the data, producing mildly more robust results than they would have been. Thus, to obtain an independent model assessment, it is essential to runit on testing data that the model has not utilized previously [80]. Here, 25% (150 points) of the total inventoried flood points were used to validate each model. The receiver operating characteristic (ROC) curve and confusion matrix were used to assess each model's performance, which are frequent and critical features used in most statistical or probabilistic applications, especially in susceptibility mapping [80; 81]. Moreover, the area under the curve (AUC), a summary of the ROC curve, was computed. ROC curve is a graphical depiction of how well locations are classified as non-events or events [81]. The AUC values range from 0 to 1, where value0 indicates a low predictive accuracy that does not accurately categorize the FFS, and value 1 indicates a perfect predictive accuracy with absolute FFS pixel categorization [81]. The kappa index was also computed to assess the model performance, where their values ranged between 0 to 1 denoting a low to a high kappa index [82].  
Confusion matrices are the primary tool for evaluating classification errors (sorting items into classes, i.e., categories or kinds of items). Machine learning under supervision is a typical application of confusion matrices. They provide the complete specification of misclassifications: the number of misclassified items for each pair of original classes to which items should be classified and incorrect class to which items are classified incorrectly. From a trusted collection of pre-classified things, it is known that items belong to an original class (a ground truth) [83].  
This study used five statistical evaluation measures to assess the trained FFS models' performance: accuracy, specificity, sensitivity, negative predictive value, and positive predictive value. Accuracy refers to the proportion of FFS and non-FFS pixels successfully classified by the resulting models. Sensitivity refers to the proportion of FFS pixels accurately detected as flood occurrences, and specificity refers to the proportion of non-FFS pixels correctly classified as non-FFS. The positive predictive value indicates the likelihood that pixels will be correctly identified as FFS, while the negative predictive value indicates the likelihood that pixels will be correctly classed as non-FFS.  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{10}\]  
\[\text{Sensitivity}=\frac{TP}{TP+FN} \tag{11}\]  
\[\text{Specificity}=\frac{TN}{FP+TN} \tag{12}\]  
\[\text{Positive predictive value}=\frac{TP}{FP+TP} \tag{13}\]  
\[\text{Negative predictive value}=\frac{TN}{FN+TN} \tag{14}\]  
where _TP_ (true positive) and _TN_ (true negative) represent the number of pixels appropriately identified, while _FP_ (false positive) and _FN_ (false negative) represent the number of pixels classified incorrectly.