## 4 Results and Discussion
### Discussion  
Table 9 shows the precision, recall, and F1-score values by rating and the average values based on the validation dataset for the ML models (building register information). F1-score is the harmonic average of precision and recall. Finally, the actual applicability of the ML models was examined by analyzing the confusion matrix results for the validation data. Overall, the precision, recall, and F1-score values of the severe rating were higher than the average values of each classifier model. This indicates that the prediction success rate for severe fire damage is higher than that for lower damage ratings. In addition, precision and recall were found to be similar. The ANN classifier model exhibited the highest performance based on the precision, recall, and F1-score averages. However, considering that the ML models were developed to predict fire damage in advance using simple information, a model with conservative predictions is considered \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Learning Model** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{ANN} & Low & 0.430 & 0.406 & 0.417 \\  & Moderate & 0.371 & 0.273 & 0.315 \\  & Severe & 0.449 & 0.595 & 0.512 \\  & Overall (average) & 0.417 & 0.425 & 0.421 \\  & Low & 0.395 & 0.282 & 0.329 \\  & Moderate & 0.370 & 0.133 & 0.196 \\  & Severe & 0.452 & 0.793 & 0.576 \\  & Overall (average) & 0.406 & 0.403 & 0.404 \\  & Low & 0.387 & 0.338 & 0.361 \\  & Moderate & 0.346 & 0.292 & 0.317 \\  & Severe & 0.462 & 0.568 & 0.510 \\  & Overall (average) & 0.399 & 0.399 & 0.399 \\  & Low & 0.431 & 0.198 & 0.271 \\  & Moderate & 0.342 & 0.191 & 0.245 \\  & Severe & 0.446 & 0.795 & 0.571 \\  & Overall (average) & 0.406 & 0.395 & 0.400 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of overall ML confusion matrix results (building register information). suitable. Therefore, utilizing the RF model with the highest recall for the severe level can be reasonable. As the ML models trained only with building register information exhibited precision, recall, and F1-score values of less than 50%, fire scenario data were included in Section 4.3 to develop ML models and examine their performance. Table 10 shows analyzing the confusion matrix results for the validation data for the ML models (building register information and fire scenario). Overall, the precision, recall, and F1-score values of the severe rating were higher than the average values of each classifier model. This indicates that the prediction success rate for severe fire damage was higher than that for lower damage ratings. In addition, precision and recall were found to be similar. The RF classifier model exhibited the highest performance based on the average precision, recall, and F1-score values. RF exhibited the highest precision (73.8%) for the test set, followed by ANN (73.7%) and DT (73%). Additionally, the RF model yielded the highest recall (74.2%), followed by ANN (73.8%) and DT (73.6%). In particular, the recall of the RF model for the severe rating, which is related to one of the important goals of this study, that is, predicting large fires, was 86%. Thus, the RF model exhibited a high overall performance. It is seen from Figure 6 that the performance of the model varies, depending on the fire scenario. Based on its total accuracy and its fair performance, RF model is suggested as the machine learning model for predicting the fire size. In order to assess the impact of input parameters on the performance of the RF model, additional analysis through a grid search algorithm was conducted to determine the importance of these parameters. This information is shown in Figure 7, where it should be noted that the total sum of all values above the horizontal bars adds up to 100%. As seen in Figure 7, structure, floor, causes, and total floor area are the critical factors that govern the fire size. It is noted that the burnt area/TFA, fire resistance structure, and season have less influence on the fire size than other parameters.