Considering the complexity of the physical model of wildfire occurrence, this paper develops a method to evaluate the wildfire risk of transmission-line corridors based on Naïve Bayes Network (NBN). First, the data of 14 wildfire-related factors including anthropogenic, physiographic, and meteorologic factors, were collected and analyzed. Then, the relief algorithm is used to rank the importance of factors according to their impacts on wildfire occurrence. After eliminating the least important factors in turn, an optimal wildfire risk assessment model for transmission-line corridors was constructed based on the NBN. Finally, this model was carried out and visualized in Guangxi province in southern China. Then a cost function was proposed to further verify the applicability of the wildfire risk distribution map. The fire events monitored by satellites during the first season in 2020 shows that 81.8% of fires fall in high- and very-high-risk regions.

In order to improve the wildfire prevention capacity of transmission lines, a wildfire risk assessment method for transmission-line corridors based on Weighted Naïve Bayes (WNB) is proposed in this paper. Firstly, the importance of 14 collected types of wildfire-related factors is analyzed based on the information gain ratio. Then, the optimal factors set and the most accurate sampling table are constructed by deleting the factors in the lowest order of importance one by one. Finally, the performance of the WNB model is compared to that of NB and BNW models according to the ROC curve and visualization. A total of 76.36% of fire events in 2020 fell in high-risk and very-high-risk regions, indicating the acceptable accuracy of the proposed assessment method of wildfire risk.