## 4 Results and Discussion
### Development of ML Models Using Building Register Information and Fire Scenarios  
As mentioned in Section 4.2, securing both precision and recall for estimating fire damage only with building information is challenging. Thus, fire scenario variables were used in addition to the data learned in Section 4.2 to develop ML models. As 830 data items out of the 12,223 data items had missing values for fire scenario data, they were excluded, and 11,393 data items were used for learning. As they corresponded to 6.8% of the total data and caused no significant change in distribution and frequency, the data analysis conducted in Section 3.4 was omitted. The abbreviations and ranges of fire scenario variables to predict fire damage are as follows:  
* Season: spg (spring), smr (summer), Fal (fall), and win (winter). * Time of day: 06-12, 12-18, 18-24, and 00-06. * Human casualties: Yes or No (1\(\leq\)). * Burnt area/TFA: 0.0 \(\leq\) A\({}_{\text{eff}}\)/A\({}_{\text{dd}}\)\(\leq\) 1.0, mean: 0.31, average: 0.72. * Ignition factor: Electrical (EL), Unknown (UNK), Mechanical (ME), Negligence (NE), Chemical (CH), etc. (arson, gas leak (explosion), traffic accidents, and natural factors)
* Ignition material: Unknown (UNK), Electrical (EL), Synthetic Resin (SR), paper and wood (P&W), waste (W), Hazardous Material (HM), etc. (fabrics, food, furniture, gas, signboards, and automobiles). * Ignition point: Living Space (LS), Facilities and Storage (FS), Function (FN), Structure (STR), Exit (Ex), Process Facility (PF), and Unknown (UNK). Figure 4 shows the distribution and frequency of the independent variables described above. The scenario variables to be used for learning are as follows. Regression analysis was conducted on the fire damage ratings included in the input and output information to identify probabilistic correlations between the input and output variables used for learning. For the regression analysis, IBM Statistics (v29.0.1.0) was used. Among the fire scenario input variables shown in Table 8, except for the season of fire and time of fire, the ignition factor, ignition material, ignition point, human casualties, and burnt area/TFA were found to significantly affect the output variable. It was shown that the season of fire probabilistically had no significant influence compared to other variables because fire evenly occurred regardless of the season. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Learning Model**} & \multicolumn{1}{c}{\(\boldsymbol{\upbeta}\)} & \multicolumn{1}{c}{\(\boldsymbol{\upbeta}\)} & \multicolumn{1}{c}{\(\boldsymbol{\upbeta}\)} & \multicolumn{1}{c}{\(\boldsymbol{\upbeta}\)} & \multicolumn{1}{c}{\(\boldsymbol{\upbeta}\)-Value} \\ \hline \multirow{8}{*}{Fire scenario} & Season of fire & 0.010 & 0.038 & 0.076 & 0.783 \\  & Time of fire & 0.039 & 0.030 & 1.699 & 0.193 \\  & Ignition factor & 0.190 & 0.031 & 37.494 & \textless{}0.001 \\  & Ignition material & 0.107 & 0.026 & 17.325 & \textless{}0.001 \\  & Ignition point & 0.208 & 0.023 & 82.593 & \textless{}0.001 \\  & Human casualties & 0.096 & 0.025 & 14.844 & \textless{}0.001 \\  & Burnt area/TFA & 0.335 & 0.026 & 161.88 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Regression analysis on input variables for output variables (fire scenario). However, ML models were developed using all selected data without probabilistic judgments among the data. The performance of each ML model was evaluated in further detail using the confusion matrix (Figure 5). The figure below shows the confusion matrix of the training and validation datasets that can compare the actual and predicted values. It was found that the ANN optimization ML model (classifier) developed earlier well predicted the severe and low ratings compared to the moderate ratings because the prediction levels of the severe and low ratings were nearly identical for both the training and validation models. Figure 4: Distribution and frequency of fire scenario information among fire data.