A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration

Jorge Pereira,Jorge S S Júnior,João Ruivo Paulo,Jérôme Mendes,Carlos Viegas

doi:10.3390/math10030300

Jorge Pereira, Jorge S S Júnior + Show 3 more

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https://doi.org/10.3390/math10030300

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Abstract

Wildfires are complex natural events that cause significant environmental and property damage, as well as human losses, every year throughout the world. In order to aid in their management and mitigate their impact, efforts have been directed towards developing decision support systems that can predict wildfire propagation. Most of the available tools for wildfire spread prediction are based on the Rothermel model that, apart from being relatively complex and computing demanding, depends on several input parameters concerning the local fuels, wind or topography, which are difficult to obtain with a minimum resolution and degree of accuracy. These factors are leading causes for the deviations between the predicted fire propagation and the real fire propagation. In this sense, this paper conducts a literature review on optimization methodologies for wildfire spread prediction based on the use of evolutionary algorithms for input parameter set calibration. In the present literature review, it was observed that the current literature on wildfire spread prediction calibration is mostly focused on methodologies based on genetic algorithms (GAs). Inline with this trend, this paper presents an application of genetic algorithms for the calibration of a set of the Rothermel model’s input parameters, namely: surface-area-to-volume ratio, fuel bed depth, fuel moisture, and midflame wind speed. The GA was validated on 37 real datasets obtained through experimental prescribed fires in controlled conditions.

Highlights

Wildfires are one of nature’s most dangerous hazards and, in the last few years, their impact has been increasing significantly, as reported by the European Commission’s20th issue of the annual wildfire report [1,2,3]
We present a review of genetic algorithm approaches for wildfire spread prediction calibration
We present the validation and results of the calibration of the Rothermel model (1) using the Algorithm 2 on real datasets obtained through experimental prescribed fires in controlled conditions

Summary

Introduction

Wildfires are one of nature’s most dangerous hazards and, in the last few years, their impact has been increasing significantly, as reported by the European Commission’s20th issue of the annual wildfire report [1,2,3]. Wildfires are one of nature’s most dangerous hazards and, in the last few years, their impact has been increasing significantly, as reported by the European Commission’s. This number is nearly four times larger than the records for the previous year (2018). Wildfires can impact ecosystems by destroying natural habitats, resources, and wildlife They cause significant damage to society, being responsible for numerous fatalities, accidents, injuries, health problems, and the destruction of human infrastructures. It is essential to direct efforts towards understanding the behavior of wildfires and improving their management. In this sense, knowledge of how wildfires propagate is critical, allowing the prediction of where the fire will be and taking the appropriate measures to mitigate its impact

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