# Development of Fire Consequence Prediction Model
## 1 Introduction
### Related Works  
Previous studies on real-time accident analysis, risk identification techniques and prediction models have investigated various methods, such as conventional risk analysis methods, dynamic risk analysis methods, and failure diagnosis and prediction techniques using artificial intelligence (AI). Signal processing, analysis techniques and fuzzy theory are generally used to determine the failure of sensors or machines. However, these methods are not readily applicable because only some methods can be used owing to the complexity of the work environment, and the difficulty in adding or modifying the algorithm for analysis and response if a new type of failure occurs. To solve this problem, [PERSON] et al. [7] presented an optimal model for classifying failure types by learning existing failure data, even without expert knowledge about sensors, using a convolutional neural network (CNN), which is a deep learning method. In addition, they developed a CNN model for classifying sensor failures into types and proposed a method for improving the model accuracy by using the ensemble technique to improve the classification performance of the CNN. Studies using deep learning have steadily increased beyond the engineering field. [PERSON] [8] developed a prediction model that supports the establishment of a damage-minimizing strategy for mobilizing effective firefighting resources and a proper response in the early stage of a disaster by predicting the risk of a fire site using early information, such as information on the building where the fire occurred and information obtained by a reporter. The researcher performed a correlation analysis of the parameters using a machine learning algorithm to investigate the parameters related to the damage size of the fire and compared the results by training and testing various algorithms. [PERSON] and [PERSON] [9] proposed a method to address the uncertainty of severity modeling for a LNG Pool fire in a study on LNG and accident severity and compare radiant heat over distance with experimental data. In addition, a study on the uncertainty of evaluating the frequency and severity of potential accident scenarios through HAZOP was proposed to increase the reliability of risk analysis by applying the risk correction index using the Fuzzy logic [10]. The conventional risk analysis method has been statistically used to consider only major events and accidents while ignoring minor events or near-misses. Recently, [PERSON] et al. [6] proposed an approach to configure a dynamic event tree/fault tree (ET/FT) using Markov modeling and solved problems related to the conventional ET/FT methodology. [PERSON] and [PERSON] [11] developed a dynamic failure probability assessment method for estimating the dynamic probability of the accident stage using near miss and accident data to perform a dynamic risk assessment of the system. [PERSON] et al. [3] used a quantitative risk assessment method to identify the potential risks to ensure safety in process systems. One of the disadvantages of quantitative risk assessment is its inability to update risks during the process life. To address this problem, the final state probability of failure data is estimated using a potential accident scenario and updated using Bayesian theory. [PERSON] and [PERSON] [11] and [PERSON] et al. [2] developed a dynamic risk assessment methodology for providing a real-time failure frequency function for processes, as shown in Figure 1. This methodology comprises three main stages for risk assessment: accident analysis, probability update, and dynamic failure assessment. In the accident-analysis stage, the event probability is estimated using an even/error tree together with a real-time process upset and other related data. Then, this probability is updated using available information. Subsequently, the updated probability is used to re-estimate the risk profile. A risk profile can be defined as an element that represents the damage caused by a potential accident scenario.