Abstract
Wildfires have significant impacts on global vegetation, wildlife, and humans. They destroy plant communities and wildlife habitats and contribute to increased emissions of carbon dioxide, nitrogen oxides, methane, and other pollutants. The prediction of wildfires relies on various independent variables combined with regression or machine learning methods. In this technical review, we describe the options for independent variables, data processing techniques, models, independent variables collinearity and importance estimation methods, and model performance evaluation metrics. First, we divide the independent variables into 4 aspects, including climate and meteorology conditions, socio-economical factors, terrain and hydrological features, and wildfire historical records. Second, preprocessing methods are described for different magnitudes, different spatial-temporal resolutions, and different formats of data. Third, the collinearity and importance evaluation methods of independent variables are also considered. Fourth, we discuss the application of statistical models, traditional machine learning models, and deep learning models in wildfire risk prediction. In this subsection, compared with other reviews, this manuscript particularly discusses the evaluation metrics and recent advancements in deep learning methods. Lastly, addressing the limitations of current research, this paper emphasizes the need for more effective deep learning time series forecasting algorithms, the utilization of three-dimensional data including ground and trunk fuel, extraction of more accurate historical fire point data, and improved model evaluation metrics.
Abstract (translated)
野火对全球植被、野生动物和人类有着显著的影响。它们破坏了植物群落和野生动物栖息地，并导致二氧化碳、氮氧化物、甲烷和其他污染物的排放增加。野火的预测依赖于各种独立变量的组合，包括回归或机器学习方法。在本文的技术审查中，我们描述了独立变量的选项、数据处理技术、模型、独立变量相关性和重要性估计方法以及模型性能评估指标。首先，我们将独立变量分为四个方面，包括气候和气象条件、社会经济因素、地形和水文特征以及野火历史记录。其次，对于不同的规模、不同的空间-时间分辨率和不同的数据格式，描述了预处理方法。第三，还考虑了独立变量的相关性和重要性估计方法。第四，我们讨论了在野火风险预测中应用统计模型、传统机器学习模型和深度学习模型的应用。在本小节中，与其它综述相比，本文特别关注了评估指标和深度学习方法的最近进展。最后，针对当前研究的局限性，本文强调了需要更有效的深度学习时间序列预测算法、利用包括地面和树干燃料在内的三维数据以及提取更精确的历史火灾点数据，以及改进模型评估指标。
URL
https://arxiv.org/abs/2405.01607
