  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.
