我国林火发生预测模型研究进展

通过文献回顾,总结了国内林火发生预测模型的研究现状,并从林火发生驱动因子、林火发生概率预测模型、林火发生频次预测模型和模型检验方法等方面进行归纳分析。得出以下结论: 1)气象、地形、植被、可燃物、人类活动等因素是影响林火发生及模型预测精度的主要驱动因子;2)林火发生概率模型中,地理加权逻辑斯蒂回归模型考虑了变量之间的空间相关性,Gompit回归模型适宜非对称结构的林火数据,随机森林模型不需要多重共线性检验,在避免过度拟合的同时提高了预测精度,是林火发生概率预测模型的优选方法之一;3)林火发生频次模型中,负二项回归模型更适合对过度离散数据进行模拟,零膨胀模型和栅栏模型可以处理林火数据中包含大量零值的问题;4)ROC检验、AIC检验、似然比检验和Wald检验方法是林火概率和频次模型的常用检验方法。林火发生预测模型研究仍是我国当前林火管理工作的重点,预测模型的选择需要依据不同地区林火数据特点。此外,构建林火预测模型时需要考虑更多的影响因素,以提高模型预测精度;未来,需要进一步探索其他数学模型在林火发生预测中的应用,不断提高林火发生预测模型的准确度。

A review of models of forest fire occurrence prediction in China

We summarized research progress of forest fire occurrence prediction model in China based on the literature review, from the prospects of forest fire drivers, models of forest fire occurrence probability, models of forest fire occurrence frequency and model validation methods. The main conclusions are: 1) Meteorology, terrain, vegetation, fuel and human activities were the main driving factors of forest fire occurrence and model prediction accuracy. 2) In the models of forest fire occurrence probability, the geographically weighted logistic regression model considered the spatial correlation between model variables, the Gompit regression model could fit the asymmetric structure fire data. The random forest algorithm had a high prediction accuracy without the requirement of multicollinearity test and excessive fitting, which made it as one of the optimal methods of forest fire occurrence probability prediction. 3) Among all the forest fire occurrence frequency models, the negative binomial regression model was suitable for fitting the over discrete data, the zero-inflated model and hurdle model could deal with fire data that contained a large number of zeros. 4) ROC test, AIC test, likelihood ratio test, and Wald test were the most common methods for evaluating the accuracy of fire occurrence probability and frequency models. The study of forest fire occurrence prediction model should be the main focus of the forest fire management. Model selection should base on fire data structure of different forests. More influencing factors should be taken into account to improve the prediction accuracy of model. In addition, it was necessary to further explore the application of other mathematical methods in forest fire prediction, to improve the accuracy of the models.