Forensic analysis of a fatal fire incident in a high-rise residential building in Bangladesh, 2010 using fire modelling

Mahmud, Shahriar; Haque, Fariha Musharrat; Rahman, Mohammad Abdur

doi:10.1063/1.5115930

AIP Conference Proceedings

2019

DOI: 10.1063/1.5115930

|View full text |Cite

Forensic analysis of a fatal fire incident in a high-rise residential building in Bangladesh, 2010 using fire modelling

Shahriar Mahmud

Fariha Musharrat Haque

Mohammad Abdur Rahman

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/1756-378X.htm 2021; Mahmud et al, 2019). For example, Harpur and peers (2013) found children below five years old playing with smoker's materials as the main culprit of fatal residential fires (Harpur et al, 2013).…”

mentioning

confidence: 99%

Fatal structure fire classification from building fire data using machine learning

Balakrishnan,

Mohammed Hashim,

Lee

et al. 2023

IJICC

View full text Add to dashboard Cite

PurposeThis study aims to develop a machine learning model to detect structure fire fatalities using a dataset comprising 11,341 cases from 2011 to 2019.Design/methodology/approachExploratory data analysis (EDA) was conducted prior to modelling, in which ten machine learning models were experimented with.FindingsThe main fatal structure fire risk factors were fires originating from bedrooms, living areas and the cooking/dining areas. The highest fatality rate (20.69%) was reported for fires ignited due to bedding (23.43%), despite a low fire incident rate (3.50%). Using 21 structure fire features, Random Forest (RF) yielded the best detection performance with 86% accuracy, followed by Decision Tree (DT) with bagging (accuracy = 84.7%).Research limitations/practical implicationsLimitations of the study are pertaining to data quality and grouping of categories in the data pre-processing stage, which could affect the performance of the models.Originality/valueThe study is the first of its kind to manipulate risk factors to detect fatal structure classification, particularly focussing on structure fire fatalities. Most of the previous studies examined the importance of fire risk factors and their relationship to the fire risk level.

show abstract

mentioning

confidence: 99%

Fatal structure fire classification from building fire data using machine learning

Balakrishnan,

Mohammed Hashim,

Lee

et al. 2023

IJICC

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Forensic analysis of a fatal fire incident in a high-rise residential building in Bangladesh, 2010 using fire modelling

Cited by 1 publication

References 8 publications

Fatal structure fire classification from building fire data using machine learning

Fatal structure fire classification from building fire data using machine learning

Contact Info

Product

Resources

About