  Citation: Bot, K.; Borges, J.G. A Systematic Review of Applications of Machine Learning Techniques for Wildfire Management Decision Support. Inventions 2022, 7, 15. https://doi.org/10.3390/ inventions7010015 Academic Editor: Anastasios Doulamis Received: 3 December 2021 Accepted: 17 January 2022 Published: 21 January 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). inventions Review A Systematic Review of Applications of Machine Learning Techniques for Wildfire Management Decision Support Karol Bot * and José G. Borges Forest Research Center and Laboratory Terra, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisboa, Portugal; joseborges@isa.ulisboa.pt * Correspondence: karolbot@isa.ulisboa.pt Abstract: Wildfires threaten and kill people, destroy urban and rural property, degrade air quality, ravage forest ecosystems, and contribute to global warming. Wildfire management decision support models are thus important for avoiding or mitigating the effects of these events. In this context, this paper aims at providing a review of recent applications of machine learning methods for wildfire management decision support. The emphasis is on providing a summary of these applications with a classification according to the case study type, machine learning method, case study location, and performance metrics. The review considers documents published in the last four years, using a sample of 135 documents (review articles and research articles). It is concluded that the adoption of machine learning methods may contribute to enhancing support in different fire management phases. Keywords: wildfires; machine learning; applications; decision support; review 1. Introduction Wildfires, e.g., uncontrolled fires occurring in forest or grassland in rural areas [1], threaten and kill people, destroy urban and rural property, degrade air quality, ravage forest ecosystems and Natura 2000 sites, and contribute to global warming. The connection between climate change and the increased risk of wildfires suggests a paradigm change in the co-existence of humans with natural catastrophes affecting the environment [2]. Indeed, the regime of wildfires in the Anthropocene is changing due to this complex fire– human–climate interaction [3]. The forest fires paradox has been highlighted by several authors [4–6]. They may play an important ecological role by removing deadwood and opening space for the growth of new vegetation. They may also release nutrients into the soil and offer ecological niches for the proliferation of wildlife. In contrast, when occurring at high intensity, forest fires lead to negative environmental impacts such as a decrease in soil quality (e.g., loss of biota, volatilization of its nutrients, and an increase in erosion). They may further contribute to a decline in biodiversity, as well as to a decrease in air quality [3,4], thus threatening forested landscapes [7]. Wildfires result from the interaction of several factors (e.g., the composition of the fuels, ignition sources, weather conditions, and topography) [8]. The landscape mosaic impacts the wildfire development process, e.g., fire ignition and frequency, rate of spread, the energy released, and the severity [3]. The complexity of the phenomenon poses a challenge to its modelling and simulation in order to address wildfire hazards proactively, i.e., to enhance silvicultural practices and forest management plans to design resilient land- scapes and to reduce loss [9–12] According to the EU Horizon 2020 Work Programme [11], the fire management cycle may be broadly segmented into three stages: (i) prevention and preparedness (pre-fire); (ii) detection and response (management of active wildfires); (iii) restoration and adaptation activities (post-wildfire). The literature discusses the re- search into methods and tools to help address each stage, as well as the policy emphasis on each [12,13]. Inventions 2022, 7, 15. https://doi.org/10.3390/inventions7010015 https://www.mdpi.com/journal/inventions