# Development of Fire Consequence Prediction Model
## 4 Conclusions  
Risk assessment is used to improve the safety of ships and prevent accidents. Recently, a method for predicting risk in real time using the dynamic risk assessment method has been proposed. In the present study, we developed an algorithm to estimate the severity of a fire accident in real time in the FGSS room of LNG bunkering and applied it to a fire accident response procedure.  
A deep learning algorithm was used in this study, and the size of the rupture was predicted based on the changes in the operating conditions of LNG bunkering in the event of a fire accident, and the severity of the accident was predicted. In the pipe rupture scenario in the LNG fuel supply line, the size of the hole when leakage occurred was predicted through changes in the LNG mass flow, LNG speed, and LNG pressure, which are LNG transfer conditions in the pipe. In the hole size prediction algorithm, four input and one output parameters were configured, and five input and two output parameters were configured in the accident-severity prediction algorithm.  
The prediction results of the hole size prediction algorithm demonstrated errors of 5.29903% and 2.87421% in the training set and test set, respectively, with an average error rate of less than 5%. In the accident-consequence prediction algorithm, the average error rate for the damage was 10% (8.62% for the training set and 12.46% for the test set), and the average error rates for the maximum-radiant-heat training set and test set were 6.25% and 6.95%, respectively.  
A method for estimating the size of a fire to be small, medium, or large was proposed to assist in the decision-making process of field workers and operators at the site in the event of a fire. The expected effect of the method is that the degree of fire consequence can be quickly determined when a fire accident occurs, and appropriate rapid response measures can be deployed to support safe and fast decision making.  
Because the algorithm proposed in this study was only applied to fires in the FGSS room of LNG-fueled ships, it is necessary to review various scenarios and operating conditions in the future.  
In the future, we aim to develop a framework for dynamic risk analysis by estimating the probability of accidents and their severity, which change in real time. To this end, it is necessary to review the features for predicting the size of the hole and the accuracy of the data that can be received in real time. In addition, we estimated the probability of accidents according to the operating conditions of the system and piping in real time. In the future, if additional data on accident probability estimation and accident severity prediction are secured and risk is quantified through dynamic risk assessment in real time, safety can be effectively enhanced.  
Conceptualization, B.-c.P.; methodology, B.-c.P. and C.L.; validation, B.-c.P. and S.-c.S.; formal analysis, C.L.; investigation, B.-c.P.; resources, S.-j.O.; data curation, B.-c.P. and S.-j.O.; writing--original draft preparation, B.-c.P. and C.L.; writing--review and editing, J.-e.L. and S.-c.S; visualization, B.-c.P. and M.-j.J.; supervision, M.-j.J. and S.-c.S.; project administration, S.-c.S.; funding acquisition, J.-e.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 2022400000090).  
The authors declare no conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.