## 4 Results and Discussion  
### Development of ML Models Using Building Register Information  
This study aimed to predict the fire size based on minimal data. In this subsection, learning was performed using only the building register information provided by the national agency among the fire data introduced in Section 3, and the precision and recall of fire damage prediction were examined. The abbreviations and ranges of building register information variables to predict fire damage are as follows. * Facility location information: MMA, MCs, and PRs. * Fire-resistant structure: Yes or No. * Industry type: Metal Machinery and Equipment Industry (MMEI), Wood Processing and Carpentry Industry (WPCI), Chemical Industry (CI), Food Industry (FI), Textile Industry (TI), Electrical and Electronics Industry (EEI), Pulp and Paper Industry (PPI), etc. (other industries). * Structure: Steel Frame Structure (SFC), Reinforced Concrete Structure (RCC), Sandwich Panel Structure (SPC), Block Structure (BLC), Container Structure (CC), Brick Structure (BC), etc. (wood, greenhouse pipe, stone, steel frame reinforced concrete, brick veneer, container, and other structures). * Number of floors: \(1\leq 30\). * Number of basement floors: \(0\leq-4\). * TFA: \(1\leq\text{A}_{\text{tf}}\leq 69,437,392\) m\({}^{2}\), mean: 12,813.30 m\({}^{2}\), standard deviation: 686,213.77 m\({}^{2}\). * Building area: \(0.03\leq\text{A}_{\text{fa}}\leq 51,226,276\) m\({}^{2}\), mean: 9366.09 m\({}^{2}\), standard deviation: 488,293.30 m\({}^{2}\). The database had a total of 12,223 data items. Figure 2 shows the distribution and frequency of the independent variables described earlier. A regression analysis was conducted on the fire damage ratings included in the input and output information to investigate probabilistic correlations between the input and output variables used for learning. In general, if the \(p\)-value of an input variable is less than 0.05, the input variable can be considered statistically important because it has a significant impact on the output variable. \begin{table}
\begin{tabular}{c c c} \hline
**ML Classifier Model** & **Advantage** & **Disadvantage** \\ \hline ANN & Excellent predictability & Limitations in interpreting the results \\ DT & Ease of interpreting the results & High probability of overfit \\ KNN & Error data does not affect results & More data significantly reduces learning speed \\ RF & Prevents overfitting for high accuracy even & Limitations in interpreting the results \\  & with a high percentage of missing values & \\ \hline \end{tabular}
\end{table} 
Table 6: Learning models’ features (advantages and disadvantages). Table 7 shows that most variables are not statistically significant except the building location information, structure, industry type, and fire-resistant structure. Compared to other input variables, the building TFA and floor area variables were unimportant in determining the fire damage rating. This is because factory facilities with small TFA or floor areas cannot significantly affect the damage rating determined by the amount of damage. Thus, small factory facilities may not be considered important in damage rating classification because they can be calculated only as small damage ratings. Figure 2: Distribution and frequency of building register information among fire data. However, ML models were developed using all selected data without probabilistic judgments between the data. The ML technologies described in the previous section were used to identify fire damage ratings from the constructed database. To use the ML models, in this study, eight variables out of the 16 variables, described in Section 3.3, were converted into input parameters. The ML code for the models, mentioned in Section 4.1, was developed using the Mathworks open source. Here, 70% of the 12,223 data items were used as training data (training set) and 30% as validation data (test set).