## 5 Conclusions  
As analyzed in Section 2, numerous studies have been conducted to predict the occurrence of fire using various machine learning (ML) methods; however, no methodology exists to predict fire damage ratings only through simple building information. Predicting fire damage using simple data can be effectively used for national and regional disaster management [39]. In this study, the capabilities of ML and artificial intelligence (AI) were explored in identifying the property damage ratings caused by factory fires. First, a database was constructed by utilizing and preprocessing the fire data provided by a national agency. In the database, 15 input parameters that can predict fire damage ratings based on the insight from past studies were generated and are as follows: facility location information, industry type, structure, fire-resistant structure, number of floors, number of basement floors, total floor area (TFA), building area, burnt area/TFA, season of fire, time of day, ignition factor, ignition material, ignition point, and human casualties. In addition, to increase the prediction and learning success rate, which is the output data, using the 15 input data, the distribution of each rating was analyzed, and the dependent  
Figure 6: Comparison of ML models. Figure 7: Relative importance of input parameters affecting fire size in RF model. variable was classified according to the property damage such that a distribution rate of approximately 33% could be obtained. The entire dataset was divided into training and test sets. The training set was used to set a prediction model, and the model's performance was evaluated through the test set. In this study, four ML models, ANN, KNN, DT, and RF, were evaluated. The performance of the models was evaluated using precision, recall, and F1-score. First, learning (a total of 12,223 data items) was performed using only building register information, and then learning (a total of 11,393 data items) was performed by adding fire scenario information to the building register information to examine the difference. The performance of the four ML models that performed learning using only building register information was less than 50%; however, the ML performance significantly improved when the four models were trained by adding fire scenario information. Among them, RF exhibited the highest accuracy for the training set, followed by ANN. However, it is difficult to predict fire damage. The proposed RF model showed a recall of 74.2% and a precision of 73.8% in identifying the degree of fire damage for the test set. Notably, it exhibited the highest recall (86%) for the severe rating among the four models. Thus, this learning model can prevent severe property damage by predicting large fires with high probability. This study demonstrates the capabilities of ML models that predict the degree of property damage in the event of a fire. Open-source data-based classification models can be used in fire centers worldwide to rapidly predict property damage. By analyzing the domestic fire incident data and setting up fire scenarios, a machine learning model using the same approach used in this study can be developed. With this model, registry information of buildings where fires have not yet occurred can be used as prediction data in order to derive property damage size. Fire damage prediction helps establish accident prevention strategies regarding disaster management [40]. It is expected that the results of the proposed prediction model will be utilized for fire prevention activities, such as the management of inspection priorities and inspection periods, while considering the fire risk rating of each building during building fire safety inspections. The novelty of this study lies in the development of ML models able to predict fire damage size quickly using basic information on buildings. To be specific, the accuracy rate of the RF model, which is around 74%, suggests a great potential of investigating large number of buildings swiftly with high probability.