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<p>Accurate fire risk prediction is crucial to mitigate the significant threats of wildfires to ecosystems, human life, and property. This article reviews various computational algorithms for predicting the risk of wildfires, highlighting their methodologies, related work, and findings. Key algorithms include Logistic Regression, Random Forest, Artificial Neural Networks, and Deep Learning models such as Convolutional Neural Networks and Long Short-Term Memory Neural Networks. The review synthesizes contributions from numerous studies to provide a comprehensive overview of the state of the art in predicting the risk of wildfires. Advances in computational technologies and the availability of large volumes of data have enabled the development of more accurate algorithms to predict and assess fire risk more accurately. Wildfires exert catastrophic effects on natural ecosystems, air quality, and human health, and the increasing frequency and intensity of these fires, intensified by climate change, underscores the need for more accurate predictive models to mitigate their impacts.</p>

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Machine learning methods for wildfire risk assessment

  • 期刊论文

  • 发表日期：

    2025年1月7日

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  • Carlos Brys,
  • David Luis La Red Martínez,
  • Marcelo Marinelli

摘要

Accurate fire risk prediction is crucial to mitigate the significant threats of wildfires to ecosystems, human life, and property. This article reviews various computational algorithms for predicting the risk of wildfires, highlighting their methodologies, related work, and findings. Key algorithms include Logistic Regression, Random Forest, Artificial Neural Networks, and Deep Learning models such as Convolutional Neural Networks and Long Short-Term Memory Neural Networks. The review synthesizes contributions from numerous studies to provide a comprehensive overview of the state of the art in predicting the risk of wildfires. Advances in computational technologies and the availability of large volumes of data have enabled the development of more accurate algorithms to predict and assess fire risk more accurately. Wildfires exert catastrophic effects on natural ecosystems, air quality, and human health, and the increasing frequency and intensity of these fires, intensified by climate change, underscores the need for more accurate predictive models to mitigate their impacts.