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.

翻译：野火对全球植被、野生动物及人类具有显著影响。它们破坏植物群落与野生动物栖息地，并导致二氧化碳、氮氧化物、甲烷等污染物排放量增加。野火预测依赖于结合回归或机器学习方法的多种自变量。本技术综述系统阐述了自变量的选择、数据处理技术、预测模型、自变量共线性与重要性评估方法以及模型性能评价指标。首先，我们将自变量归纳为四个维度：气候气象条件、社会经济因素、地形水文特征及历史火灾记录。其次，针对不同量纲、不同时空分辨率及不同格式的数据，阐述了相应的预处理方法。第三，探讨了自变量的共线性分析与重要性评估方法。第四，系统论述了统计模型、传统机器学习模型及深度学习模型在野火风险预测中的应用。本部分相较于现有综述，特别深入讨论了深度学习方法的评价指标与最新进展。最后，针对当前研究的局限性，本文强调需要开发更有效的深度学习时间序列预测算法，利用包含地表与树干燃料的三维数据，提取更精确的历史火点数据，并完善模型评估指标体系。