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Wildfire is a primary forest disturbance. A better understanding of wildfire susceptibility and its dominant influencing factors is crucial for regional wildfire risk management. This study performed a wildfire susceptibility assessment using multiple methods, including logistic regression, probit regression, an artificial neural network, and a random forest (RF) algorithm. Yunnan Province, China was used as a case study area. We investigated the sample ratio of ignition and nonignition data to avoid misleading results due to the overwhelming number of nonignition samples in the models. To compare model performance and the importance of variables among the models, the area under the curve of the receiver operating characteristic plot was used as an indicator. The results show that a cost-sensitive RF had the highest accuracy (88.47%) for all samples, and 94.23% accuracy for ignition prediction. The identified main factors that influence Yunnan wildfire occurrence were forest coverage ratio, month, season, surface roughness, 10 days minimum of the 6 h maximum humidity, and 10 days maxima of the 6 h average and maximum temperatures. These seven variables made the greatest contributions to regional wildfire susceptibility. Susceptibility maps developed from the models provide information regarding the spatial variation of ignition susceptibility, which can be used in regional wildfire risk management.
Cao et al. (Thu,) studied this question.