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Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by Fireopen access

Authors
Lee, JonghoShin, JiukLee, JaewookPark, ChorongSohn, Dongwook
Issue Date
Nov-2023
Publisher
MDPI
Keywords
fire occurrence; property damage prediction; machine learning; data based; disaster prevention
Citation
APPLIED SCIENCES-BASEL, v.13, no.21
Indexed
SCIE
SCOPUS
Journal Title
APPLIED SCIENCES-BASEL
Volume
13
Number
21
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/68614
DOI
10.3390/app132111866
Abstract
Large fires in factories cause severe human casualties and property damage. Thus, preparing more economical and efficient management strategies for fire prevention can significantly improve fire safety. This study deals with property damage grade prediction by fire based on simplified building information. This paper's primary objective is to propose and verify a framework for predicting the scale of property damage caused by fire using machine learning (ML). Korean public datasets are collected and preprocessed, and ML algorithms are trained with only 15 input data using building register and fire scenario information. Four models (artificial neural network (ANN), decision tree (DT), k-nearest neighbor (KNN), and random forest (RF)) are used for ML. The RF model is the most suitable for this study, with recall and precision of 74.2% and 73.8%, respectively. Structure, floor, causes, and total floor area are the critical factors that govern the fire size. This study proposes a novel approach by utilizing ML models to accurately and rapidly predict the size of fire damage based on basic building information. By analyzing domestic fire incident data and creating fire scenarios, a similar ML model can be developed.
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