School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China.
Electric Power Research Institute, Guangdong Power Grid, Guangzhou 510080, China.
Sensors (Basel). 2021 Jan 18;21(2):634. doi: 10.3390/s21020634.
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.
考虑到野火发生的物理模型的复杂性，本文提出了一种基于朴素贝叶斯网络（NBN）评估输电线路走廊野火风险的方法。首先，收集并分析了包括人为、地形和气象因素在内的 14 个与野火相关的因素的数据。然后，利用 Relief 算法根据各因素对野火发生的影响程度对其进行重要性排序。在依次剔除最不重要的因素后，基于 NBN 构建了一个最优的输电线路走廊野火风险评估模型。最后，在中国南部的广西省进行了该模型的实现和可视化，并提出了一个代价函数来进一步验证野火风险分布图的适用性。2020 年第一季通过卫星监测到的火灾事件表明，81.8%的火灾发生在高风险和极高风险区域。
