Forest fire induced Natech risk assessment

A survey of geospatial technologies

Journal article (2019)

Authors

Mohsen Naderpour University of Technology Sydney
Hossein Mojaddadi Rizeei University of Technology Sydney
N. Khakzad Rostami Safety and Security Science - TPM
Biswajeet Pradhan Sejong University, University of Technology Sydney
Research Group
Safety and Security Science (TPM) (TU Delft)
Risk Forest fire Geospatial information system Industrial plants Natech
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Published Date

2019

Language

English

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Faculty

Technology, Policy and Management

Department

Values Technology and Innovation

Research Group

Safety and Security Science

Abstract

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.