# Predicting the Duration of Forest Fires Using Machine Learning Methods
###### Abstract
In this research paper, Figure 1 outlines the incorporation of machine learning methods in wildfire management. ML models, a subset of artificial intelligence, harness the power of data and algorithms to learn from past experiences [38]. In the context of wildfire management, these models are invaluable, analyzing historical data and leveraging it to create predictive models capable of forecasting the spread of future fires. Although more complex than their statistical and physical counterparts, these models stand out for their ability to incorporate a broad array of variables. Understanding the requirements of ML models is crucial, particularly in terms of the variables they depend on for accurate predictions [39]. This research work focuses on the use of machine learning techniques to predict the duration of forest fires which occurred in Greece from 2014 to 2023. The data were collected by the Hellenic Fire Service, and then, after clearing missing records, the data were digitized and one of three categories was assigned to every pattern: fires of short duration, fires of medium duration, and fires of long duration. Figure 2 shows the burned areas in Greece per year for the last 10 years, according to the Global Wildfire Information System (GWIS). [PERSON] et al. pointed out the vulnerability of the Greek ecosystem (pine forest) to large forest fires, along with other Mediterranean countries, such as Spain, Portugal, Italy, and France [40]. The fires in the Mediterranean region have become very intense and dangerous, with scientists reporting a sixth-generation megafire clearly linked to global change. This new type of fire broke out for the first time in Portugal and Spain in 2017, with over 120 deaths, and the next year in Greece, with 104 deaths. The common characteristics of this new type of fire are it is extreme, uncontrollable, and lethal, as indicated in the WWF report of 2019, available from http://awss assets.panda.org/downloads/wwf_the_mediterranean_burns_2019_eng_final.pdf (accessed on 21 October 2024). In order to improve preparation for fire hazards, this paper improves forest fire management through the use of machine learning methods. The prediction of the duration of a fire is important, as in this way, on the one hand, an estimate can be made of the expected damage that will be caused in the area, and on the other, the human resources required to extinguish the fire can be calculated. Similar works in this area include the work of [PERSON] et al. that used the duration of a wildfire and the burnt area to determine the scale of wildfires using neural networks [41]. Also, [PERSON] et al. proposed a surrogate model [42] to model the size of a wildfire over time, using data collected from wildfires in Tasmania. Furthermore, [PERSON] et al. proposed [43] the application of joint mixture models to model the duration and the size of wildfires. In this work, a number of machine learning models, which have been successfully tested on a wide range of problems in the modern literature, were used. The purpose of these models is the satisfactory separation of the categories of the problem through stochastic techniques that adjust the parameters of the above models. In this paper, we review the application of ML in forest fire management. Our main overall objective is to improve awareness of ML methods among fire researchers and managers, and illustrate the open data that the Hellenic Fire Service provide. In addition to that, to highlight the current state-of-the-art methods in predicting forest fire dur  
Figure 1: Machine learning techniques used in wildfire management. Figure 2: Burned area in Greece for the last ten years. tion, and their benefit to decision making regarding the fire fighting resources required for firefighters. The US Forest Service underlines the importance of predictive services that give information to fire managers in order to anticipate and determine the resource needs, such as firefighters, engines, airplanes, etc. [44].