## 2 Literature Review
### Research Trends in Firefighting  
Firefighting research has generally focused on data collection and prediction to facilitate a swift response of fire authorities in the event of a fire [12]. First, for accuracy in fire prediction, research was conducted on new algorithms to predict fire risks for properties based on ML. The data required for statistical learning must be composed of numerical data, such as variables that affect fire occurrence and reaction variables that indicate the fire frequency. Algorithms have been implemented through statistical ML for the numerical data. Further, ML and deep learning have been used, including several datasets, such as fire management, effective response to fire, fire spread prediction, and detection. It was proven that implementing algorithms that use other frameworks' data can accurately predict fire occurrence [13]. In addition to statistical ML, FireCast, a new system that combines AI technology with geographic information systems (GIS) data collection strategies, can predict the areas around burning forest fires prone to high fire risks in the foreseeable future. FireCast outperformed the random prediction model and Farsie, a commonly used forest fire diffusion model, in terms of total accuracy, recall, and F-score [14]. Further, studies have focused on fire prediction systems through image recognition and prediction methods through ML by constructing numerical datasets. Multi-sensor detection systems were combined with image recognition to implement rapid and stable smart fire detection systems, and research was conducted to extract and classify important features from the existing images collected in real environments using ML. Studies have also attempted to identify the structure of buildings in the event of a fire to utilize the prediction systems of fire authorities [15]. An FE-based ML framework was developed to predict structural response to fire in real time based on the temperature data of structural members, and a numerical database was constructed for steel structures that are affected by hundreds of fire scenarios. Structural response to fire was simulated in ABAQUS using the FE method. The FE-based ML framework developed in the study can predict the real-time response of a structure to fire using the ML model based on the FE database. This verified that it can supplement the considerable time consumption of the traditional FE method when applied to a fire emergency. These studies are expected to aid officials and fire authorities in managing resources more efficiently. Further, they can facilitate the prevention of disasters by proposing optimized models focused on risks using statistical ML and indexing for fire risk assessment. Large fires further increase human casualties and property damage, and studies on classifying previous fires through data analysis that applied ML and predicting fires in advance have been conducted to prevent them. Numerous countries have constructed fire databases that can be used to predict and manage fires. AI-based research, which combines various types of data (e.g., images, videos, and big data) with fire data and performs training using AI, can improve fire detection and prediction accuracy. This can aid in minimizing the risks or damage of fires that may occur in urban spaces in the future. The study advances the previous literature by proposing and verifying a framework for predicting the property damage scale caused by fire using machine learning (ML). While previous studies have focused on predicting the occurrence of fires, this study specifically addresses the prediction of property damage ratings based on simple building information. As this study aimed to develop a framework for prior response by predicting fire damage using the relatively simple and numerical information of buildings, it is significant because it enables rapid and swift fire damage prediction.