# Predicting the Duration of Forest Fires Using Machine Learning Methods
###### Abstract
Also, the European State Forest Association, states that addressing the challenge of forest fires requires a concerted effort that combines scientific research, practical management strategies, and strong community engagement [45]. Therefore, technology has become a valuable ally for the environmental sciences. On that, recent reviews demonstrate the increase, in the last ten years, in the application of ML models in the environmental sciences [46] and forest ecology [47]. The Canadian specialist in fire science, [PERSON], also points out that a better method of wildfire prediction is crucial for wildfire management. Consequently, there has been a growing interest in the use of machine learning (ML) methodologies in wildfire science and management in recent years [37]. Forest fires and their management is a unique science field, with six problem domains according to [PERSON] et al. These are fire detection, fuel characterization, and mapping; climate change and fire weather; fire susceptibility, occurrence, and risk; fire behavior prediction; fire effects; and fire management. In our paper, we focus on fire management, because there appears to be few studies in this domain problem, according to [PERSON] et al. [PERSON] showed that fire management is a type of risk management that aims to maximize fire benefits while minimizing costs and losses [48]. Fire management decisions are crucial on a variety of scales, including long-term strategic decisions about resource procurement and location control in large regions; medium-term tactical decisions about resource acquisition, relocation, or release during the fire season; and short-term real-time operational decisions regarding resource deployment and usage on specific events [37]. [PERSON] indicates that fire management groups struggle to effectively respond in a limited amount of time. It would be wise to keep an eye out for potential large fires [49]. This paper presents a fast-decision model for predicting the duration of ongoing fires. As a result, this review will help practitioners and researchers in the wildfire community who are interested in using machine learning techniques by offering guidance and information. It will also give ML researchers the chance to find potential uses in the field of wildfire science and management. The field of wildfire duration prediction is limited, as researchers tend to focus more on fire occurrence and early detection. The domain we are focusing on appears to have great potential in two ways: first, in wildfire management; and second, for machine learning researchers. The objective of this paper is to introduce an innovative approach by incorporating the number of firefighters, vehicles, and aerial forces used in each of the more than 72,000 fire incidents as key data points. Our research stands out by focusing not only on the occurrence of fires but also on the critical role of human and material resources in managing them. The application of machine learning (ML), and especially the Random Forest algorithm, in our project has proven highly valuable, enabling us to accurately analyze and predict critical parameters such as fire duration, while also considering human and material resources. Our findings are reliable, offering substantial support for optimizing wildfire management strategies. Our contribution can enhance the understanding of both material needs and human resources for fighting a wildfire, at both the local level and the European level, through the European Civil Protection Mechanism. The rest of this article is organized as follows: In Section 2, the used dataset is described, as well as the incorporated machine learning methods; in Section 3, the experimental results are fully described; and finally, in Section 4 some conclusions are discussed, accompanied by some guidelines for future research.