Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by Fire
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 infor...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/21/11866 |
| _version_ | 1797632177345658880 |
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| author | Jongho Lee Jiuk Shin Jaewook Lee Chorong Park Dongwook Sohn |
| author_facet | Jongho Lee Jiuk Shin Jaewook Lee Chorong Park Dongwook Sohn |
| author_sort | Jongho Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T11:33:13Z |
| format | Article |
| id | doaj.art-000fb3944337418a9f73b8886ecd9ecf |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T11:33:13Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-000fb3944337418a9f73b8886ecd9ecf2023-11-10T14:58:59ZengMDPI AGApplied Sciences2076-34172023-10-0113211186610.3390/app132111866Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by FireJongho Lee0Jiuk Shin1Jaewook Lee2Chorong Park3Dongwook Sohn4Korea Institute of Civil Engineering & Building Technology, Goyang 10223, Republic of KoreaDepartment of Architectural Engineering, Gyeongsang National University, Jinju 52828, Republic of KoreaKorea Institute of Civil Engineering & Building Technology, Goyang 10223, Republic of KoreaDepartment of Architecture & Architectural Engineering, Yonsei University, Seoul 03722, Republic of KoreaDepartment of Architecture & Architectural Engineering, Yonsei University, Seoul 03722, Republic of KoreaLarge 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.https://www.mdpi.com/2076-3417/13/21/11866fire occurrenceproperty damage predictionmachine learningdata baseddisaster prevention |
| spellingShingle | Jongho Lee Jiuk Shin Jaewook Lee Chorong Park Dongwook Sohn Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by Fire Applied Sciences fire occurrence property damage prediction machine learning data based disaster prevention |
| title | Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by Fire |
| title_full | Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by Fire |
| title_fullStr | Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by Fire |
| title_full_unstemmed | Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by Fire |
| title_short | Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by Fire |
| title_sort | development of a data based machine learning model for classifying and predicting property damage caused by fire |
| topic | fire occurrence property damage prediction machine learning data based disaster prevention |
| url | https://www.mdpi.com/2076-3417/13/21/11866 |
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