## 5 Results and Discussion
### Assessing the Capability of FPI to Represent Flood Spatial Dynamics  
In this section, the ROC curve was constructed by varying the threshold of the Flood Potential Index (FPI) to evaluate the model's performance for flood events. The True Positive Rate (TPR) and False Positive Rate (FPR) were calculated for each threshold, with the AUC calculated according to Formula (11), serving as a summary metric. The ROC curve generated for predicting flood events using the FPI demonstrates the efficacy of the model in distinguishing between flood and non-flood scenarios, as shown in Figure 9.  
The ROC curve in Figure 9 provides a visual representation of the trade-off between TPR and FPR across different classification thresholds. In the generated ROC curve, the model shows a rapid increase towards the top-left corner of the plot, indicating that it achieves a high TPR at relatively low FPR values. This characteristic is highly desirable in flood prediction scenarios, as it implies that the model can accurately predict flood events while minimizing false alarms. The ROC curve's proximity to the top-left corner suggests strong predictive capability, indicating that the model effectively  
Figure 9: (**a**) ROC curve for predicting flood events using FPI index; (**b**) comparison of FPI index distribution between flood and non-flood events.  
presence and absence of flood events. The AUC value of 0.854 further supports the model's robustness, suggesting that the logistic regression model used here has excellent predictive accuracy for flood events based on the FPI index.  
For a better analysis of the FPI's capability to capture flood events using different datasets, this study defines a high flood potential when the FPI is equal to or above 0.65. Flood events are classified based on whether the FPI exceeds this threshold. The FPI index successfully identifies 58 true positive flood events and 150 true negative events, while it records 30 false positives and 23 false negatives. The performance of the flood prediction model using FPI is evaluated using several key metrics. The model achieves a predictive accuracy of 79.7%, indicating that nearly 80% of the predictions are correctly identified. TPR is 71.6%, suggesting that the model successfully identifies 71.6% of actual flood events. FPR is relatively low at 16.7%, showing that only a small proportion of non-flood events are incorrectly classified as floods. NPV is higher at 86.7%, indicating a strong capability in correctly predicting the absence of flood events. Additionally, the model's specificity is 83.3%, highlighting its effectiveness in accurately identifying non-flood events.  
Moreover, the box plot demonstrates a clear distinction between the FPI values for flood and non-flood events. The box plot visualizes the distribution of the FPI for two categories: flood events (labeled as 1) and non-flood events (labeled as 0). The plot reveals several insights regarding the relationship between the FPI index and the occurrence of flood events. Firstly, the median value of the FPI for flood events is significantly higher compared to that for non-flood events. The median line for flood events lies well above 0.6, whereas the median for non-flood events is below 0.3. This indicates that the FPI tends to be higher during months with flood occurrences, suggesting that the FPI index is positively correlated with flood risk.  
In conclusion, the ROC curve and the corresponding AUC value indicate that the logistic regression model is effective in predicting flood events based on the FPI index. The model's strong discriminatory power, reflected by an AUC of 0.89, suggests a good balance between sensitivity and specificity. These metrics collectively indicate that the FPI index, when used with an optimal threshold, provides a reasonably reliable tool for flood prediction, though further refinement could enhance its predictive capabilities.