## 3 Material and Methods
### ML Classifier Model Overview  
In this study, four machine learning models were used. First, the artificial neural network (ANN) model is an ML methodology that describes the learning process of the human brain using mathematical and probabilistic methodologies. It comprises input, hidden, and output layers. Each layer has multiple nodes, and each node is combined with one or more other nodes. The nodes are connected complexly, delivering information on weight and bias values. The activation function converts the sum of the weight values into an output signal and transmits the related information to the next layer [17]. Examples that use this technique can be found in [18; 19; 20; 21]. The second model is the decision tree (DT) model, an AI algorithm implemented with a tree-shaped model. It learns patterns existing between data by analyzing the data and estimates results by combining them. It performs learning by forming a tree structure from upper to lower nodes and selecting classification variables and criteria for each stage. The depth can be considered a representative hyper-parameter; however, overfitting is highly likely to occur in the training dataset with increased depth [22]. Examples that use this technique can be found in [23; 24; 25; 26]. The third model is k-nearest neighbor (KNN). The KNN algorithm examines ambient data and classifies them into many data-containing categories. It is used assuming that data with similar characteristics can be included in similar categories. The algorithm's performance changes significantly depending on the k value, which indicates the number of ambient data. The k value has the most significant impact on learning performance [27]. Overfitting occurs with an increased k value because it is difficult to clearly express the features of the data. In contrast, overfitting may occur with the decrease in the k value under the influence of certain data [28]. Examples that use this technique can be found in [29; 30; 31]. The final model is the random forest (RF) model. Ensemble learning is the method of learning data using multiple learning models rather than a single ML model, and representative methods include voting, bagging, boosting, and staking [32]. The RF methodology is included in the bagging method. It is an ensemble model for DT and a collective model that calculates results by combining multiple decision trees with different characteristics. It exhibits high accuracy and can be used as a solution to the overfitting issue found in the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Variable Name** & **Feature** & **Variable Type** & **Type of Use** \\ \hline
1 & Property damage rating & Output & Continuous & Independent \\
2 & Facility location information & Building register information & Continuous & Independent \\
3 & Number of floors & Building register information & Continuous & Independent \\
4 & Number of basement floors & Building register information & Continuous & Independent \\
5 & TFA & Building register information & Continuous & Independent \\
6 & Building area & Building register information & Continuous & Independent \\
7 & Structure & Building register information & Categorical & Independent \\
8 & Industry type & Building register information & Categorical & Independent \\
9 & Fire-resistant structure & Building register information & Categorical & Independent \\
10 & Season of fire & Fire scenario & Categorical & Independent \\
11 & Time of fire & Fire scenario & Categorical & Independent \\
12 & Ignition factor & Fire scenario & Categorical & Independent \\
13 & Ignition material classification & Fire scenario & Categorical & Independent \\
14 & Ignition point classification & Fire scenario & Categorical & Independent \\
15 & Human casualties & Fire scenario & Categorical & Independent \\
16 & Burnt area/TFA & Fire scenario & Continuous & Independent \\ \hline \hline \end{tabular}
\end{table} 
Table 5: List of variables used in the classification model for the property damage caused by fire. DT method [33].