A review of machine learning applications in wildfire science and management

Abstract

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildfire science and management. Our overall objective is to improve awareness of ML methods among wildfire researchers and managers, as well as illustrate the diverse and challenging range of problems in wildfire science available to ML data scientists. To that end, we first present an overview of popular ML approaches used in wildfire science to date, and then review the use of ML in wildfire science as broadly categorized into six problem domains, including: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. Furthermore, we discuss the advantages and limitations of various ML approaches relating to data size, computational requirements, generalizability, and interpretability, as well as identify opportunities for future advances in the science and management of wildfires within a data science context. In total, we identfied 300 relevant publications up to the end of 2019, where the most frequently used ML methods across problem domains included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. As such, there exists opportunities to apply more current ML methods — including deep learning and agent based learning — in the wildfire sciences, especially in instances involving very large multivariate datasets. We must recognize, however, that despite the ability of ML methods to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods, such as deep learning, requires a dedicated and sophisticated knowledge of their application. Finally, we stress that the wildfire research and management communities play an active role in providing relevant, high quality, and freely available wildfire data for use by practitioners of ML methods.

Open Data and Machine Learning to Model the Occurrence of Fire in the Ecoregion of “Llanos Colombo–Venezolanos”

“…In a recent review document [3] about the applications of machine learning in forest fire science and management, 298 publications were identified (between the years 1996 and 2019), with an important increase during the past 5 years. Among the references, in 71 cases machine learning algorithms were implemented to identify areas susceptible to the occurrence of fire events.…”

“…However, uncontrollable fires (wildfires) often represent a major threat to public safety, infrastructure, biodiversity, and forest resources [2]. Each year billions of dollars are spent on fire control, which ultimately aims to mitigate or prevent the negative effects of wildfires [3]. It is estimated that 420 Mha of land are burned each year globally [4], mainly in savannahs and grasslands [3].…”

“…Each year billions of dollars are spent on fire control, which ultimately aims to mitigate or prevent the negative effects of wildfires [3]. It is estimated that 420 Mha of land are burned each year globally [4], mainly in savannahs and grasslands [3]. Climate change, reduced rainfall, increased temperature, prolonged dry seasons, and the impact of human activity have increased the potential for forest fires in many regions of the world [5,6] and there is evidence of an increase in the frequency, size, and severity of forest fires, in addition to a consequent increase in the costs associated with controlling them [7].…”

“…In the science and management of fire, there has been a growing interest and evolution of methodological approaches focused on data (machine learning), in which knowledge is extracted directly from the data [3]. Machine learning methodologies are based on algorithms capable of learning from and making predictions about the data, and modeling hidden relationships between a set of input and output variables, which in this case represent the factors associated with the occurrence of fires (independent variable) and the presence/absence of fires (dependent variable) [10].…”

Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method

“…Among traditional methods, logistic regression (LR) is the most common when dealing with binary outcomes, like the presence or absence of fire [24]. Conversely, machine learning (ML) methods, as a form of artificial intelligence, are widely used in wildfire science and management, with more than 300 articles published on this topic since the 1990s [25]. Random forest (RF) belongs to the decision trees branch of the same group of ML methods [26].…”