Keywords
ldfire spread; wildfire risk estimation; machine learning; human factors; natural factors
Start Date
7-7-2022 1:00 PM
End Date
7-7-2022 1:20 PM
Abstract
Wildfires can be very dangerous and destructive. In 2021, states in the USA particularly affected by deadly and damaging wildfires include California, Oregon, Montana, Washington, and Arizona (statista.com, 2021). Worldwide, countries recently affected significantly by devastating wildfires include, but are not limited to, Algeria, Australia, Brazil, Canada, Greece, India, Spain, South Korea, and Turkey. Thus, two important research questions are “how to predict fire spread behaviours?” and “how to evaluate wildfire risks?”. To answer these questions, in this paper we conducted a survey of recent methods and approaches that are based on machine learning techniques. For the first research question, fire spread is an important element of fire behaviours. Based on our survey, fire spread models can be categorised into three groups: traditional physical models (based on physics), data-driven models (based on machine learning techniques), and hybrid models (that combine both physical models and machine learning techniques). Regarding the second research question, it is challenging to quantify fire risks, particularly the wildfire probability and the fire risk zone, because numerous factors are involved, including human factors and natural factors. In this paper, we surveyed existing wildfire research work that involved human and natural factors. Human factors, such as human presence and socioeconomic transformations, and natural factors, such as weather and geographic information, were usually selected and leveraged to predict wildfire probability. We surveyed related journal and conference articles published recently, organised them in two taxonomies (ontology trees) pertaining to each of the two research questions addressed, provided comprehensive discussions for both questions, and suggested several possible directions of future work. Among the most significant results obtained, we found out that (i) For the fire spread behaviour prediction problems, traditional physical models play an important role but can be improved with machine learning techniques. For example, machine learning techniques are commonly used to assist traditional physical modelling for more accurate results and less time consumptions; and (ii) For the risk estimation problems, machine learning is commonly and effectively used for modelling the connections between human and natural factors and wildfire burned areas as well as for quantifying fire risks.
A Survey of Wildfire Spread Prediction and Risk Estimation Methodswith Machine Learning Techniques
Wildfires can be very dangerous and destructive. In 2021, states in the USA particularly affected by deadly and damaging wildfires include California, Oregon, Montana, Washington, and Arizona (statista.com, 2021). Worldwide, countries recently affected significantly by devastating wildfires include, but are not limited to, Algeria, Australia, Brazil, Canada, Greece, India, Spain, South Korea, and Turkey. Thus, two important research questions are “how to predict fire spread behaviours?” and “how to evaluate wildfire risks?”. To answer these questions, in this paper we conducted a survey of recent methods and approaches that are based on machine learning techniques. For the first research question, fire spread is an important element of fire behaviours. Based on our survey, fire spread models can be categorised into three groups: traditional physical models (based on physics), data-driven models (based on machine learning techniques), and hybrid models (that combine both physical models and machine learning techniques). Regarding the second research question, it is challenging to quantify fire risks, particularly the wildfire probability and the fire risk zone, because numerous factors are involved, including human factors and natural factors. In this paper, we surveyed existing wildfire research work that involved human and natural factors. Human factors, such as human presence and socioeconomic transformations, and natural factors, such as weather and geographic information, were usually selected and leveraged to predict wildfire probability. We surveyed related journal and conference articles published recently, organised them in two taxonomies (ontology trees) pertaining to each of the two research questions addressed, provided comprehensive discussions for both questions, and suggested several possible directions of future work. Among the most significant results obtained, we found out that (i) For the fire spread behaviour prediction problems, traditional physical models play an important role but can be improved with machine learning techniques. For example, machine learning techniques are commonly used to assist traditional physical modelling for more accurate results and less time consumptions; and (ii) For the risk estimation problems, machine learning is commonly and effectively used for modelling the connections between human and natural factors and wildfire burned areas as well as for quantifying fire risks.
Stream and Session
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