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Evaluating the severity of building fires with the analytical hierarchy process, big data analysis, and remote sensing
- Source :
- Natural Hazards. 103:1843-1856
- Publication Year :
- 2020
- Publisher :
- Springer Science and Business Media LLC, 2020.
-
Abstract
- This study assessed the severity of building fires in 17 villages that comprise Taishan District, New Taipei City, Taiwan. A literature review was performed to discuss the impact of fire severity assessment criteria in order to develop items and factors for the analytic hierarchy process (AHP). We identified six items for the building fire severity assessment: rescue response time, narrow road density, water-sacristy area density, building risk, hazardous materials’ place density, and fire safety inspection-regulated premises density. Big data analysis and remote sensing were employed to facilitate devising the AHP structure with items and factors. We also compared the annual average burned area from 2005 to 2015 through the building fire severity assessment to validate assessment accuracy. The actual yearly average burned area in each village of Taishan District was used to verify the building fire severity assessment, and the compliance rate of the rating (i.e., high, moderate, and low) was, respectively, 60%, 67%, and 67%. The proposed assessment is evidently feasible and can act as a reference for quantitative analyses for assessments of building fire severity.
- Subjects :
- 021110 strategic, defence & security studies
Atmospheric Science
010504 meteorology & atmospheric sciences
business.industry
Big data
0211 other engineering and technologies
Annual average
Analytic hierarchy process
macromolecular substances
02 engineering and technology
Fire safety
01 natural sciences
Severity assessment
Remote sensing (archaeology)
Natural hazard
Earth and Planetary Sciences (miscellaneous)
Environmental science
business
0105 earth and related environmental sciences
Water Science and Technology
Remote sensing
Subjects
Details
- ISSN :
- 15730840 and 0921030X
- Volume :
- 103
- Database :
- OpenAIRE
- Journal :
- Natural Hazards
- Accession number :
- edsair.doi...........22b593db6028be956c761333da474922