TY - JOUR
T1 - Forest fire induced Natech risk assessment
T2 - A survey of geospatial technologies
AU - Naderpour, Mohsen
AU - Rizeei, Hossein Mojaddadi
AU - Khakzad, Nima
AU - Pradhan, Biswajeet
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Forest fire
KW - Geospatial information system
KW - Industrial plants
KW - Natech
KW - Risk
UR - http://www.scopus.com/inward/record.url?scp=85068234941&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2019.106558
DO - 10.1016/j.ress.2019.106558
M3 - Article
AN - SCOPUS:85068234941
SN - 0951-8320
VL - 191
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
M1 - 106558
ER -