Document


Title

Forest fire induced Natech risk assessment: a survey of geospatial technologies
Document Type: Journal
Author(s): Mohsen Naderpour ; Hossein Mojaddadi Rizeei ; Nima Khakzad ; Biswajeet Pradhan
Publication Year: 2019

Cataloging Information

Keyword(s):
  • forest fires
  • geospatial information
  • industrial plants
  • literature review
  • Natech
Record Maintained By:
FRAMES Staff; catalog@frames.gov
Record Last Modified: July 10, 2019
FRAMES Record Number: 58207

Description

Forest fires threaten a large part of the world's forests, communities, and industrial plants, triggering technological accidents (Natechs). Forest fire modelling with respect to contributing spatial parameters is one of the well-known ways not only to predict the fire occurrence in forests, but also to assess the risk of forest-fire-induced Natechs. This study is a review of methods based on geospatial information system (GIS) for modelling forest fires and their potential Natechs that have been implemented all over the world. The present study conducts a systematic literature review of the methods used for forest fire susceptibility, hazard, and risk assessment, while dividing them into four general categories: (a) statistical and data-driven models; (b) machine learning models; (c) multi-criteria decision-making models, and (d) ensemble models. In addition, some forest fire detection techniques using satellite imagery are reviewed. A comparison is also conducted to highlight the research gaps and required future research. The results of the present research assist decision makers to select the most appropriate techniques according to specific forest conditions. Results show that data-driven approaches are the most frequently applied methods while ensemble approaches are more accurate.

Online Link(s):
Citation:
Naderpour, Mohsen; Rizeei, Hossein Mojaddadi; Khakzad, Nima; Pradhan, Biswajeet. 2019. Forest fire induced Natech risk assessment: a survey of geospatial technologies. Reliability Engineering & System Safety online early.