Preventing and suppressing forest fires is one of the main tasks of forestry agencies to reduce resource loss and requires a thorough understanding of the importance of factors affecting their occurrence. This study was carried out in forest plantations on Maoer Mountain in order to develop models for predicting the moisture content of dead fine fuel using meteorological and soil variables. Models by Nelson (Can J For Res 14:597–600, 1984) and Van Wagner and Pickett (Can For Service 33, 1985) describing the equilibrium moisture content as a function of relative humidity and temperature were evaluated. A random forest and generalized additive models were built to select the most important meteorological variables affecting fuel moisture content. Nelson's (Can J For Res 14:597–600, 1984) model was accurate for Pinus koraiensis, Pinus sylvestris, Larix gmelinii and mixed Larix gmelinii—Ulmus propinqua fuels. The random forest model showed that temperature and relative humidity were the most important factors affecting fuel moisture content. The generalized additive regression model showed that temperature, relative humidity and rain were the main drivers affecting fuel moisture content. In addition to the combined effects of temperature, rainfall and relative humidity, solar radiation or wind speed were also significant on some sites. In P. koraiensis and P. sylvestris plantations, where soil parameters were measured, rain, soil moisture and temperature were the main factors of fuel moisture content. The accuracies of the random forest model and generalized additive model were similar, however, the random forest model was more accurate but underestimated the effect of rain on fuel moisture.

预防和扑灭森林大火是林业机构减少资源损失的主要任务之一，需要彻底了解影响其发生的因素的重要性。这项研究是在猫儿山的人工林中进行的，目的是建立利用气象和土壤变量预测死细燃料水分含量的模型。评估了纳尔逊（Can J For Res 14：597–600，1984）和Van Wagner and Pickett（Can For Service 33，1985）的模型，该模型描述了平衡含水量与相对湿度和温度的关系。建立了随机森林和广义添加剂模型，以选择影响燃料含水量的最重要的气象变量。尼尔森（Can J For Res 14：597–600，1984）模型对于红松，樟子松，落叶松和混合落叶松—榆树的燃料。随机森林模型表明温度和相对湿度是影响燃料含水量的最重要因素。广义加性回归模型表明，温度，相对湿度和雨水是影响燃料含水量的主要驱动因素。除了温度，降雨和相对湿度的综合影响外，在某些地方太阳辐射或风速也很重要。在P. koraiensis和P. sylvestris中在测量土壤参数的人工林中，降雨，土壤湿度和温度是燃料含水量的主要因素。随机森林模型和广义加性模型的精度相似，但是，随机森林模型更准确，但低估了雨水对燃料水分的影响。