A Spatial Assessment of Wildfire Risk for Transmission-Line Corridor Based on a Weighted Naïve Bayes Model

Kunxuan Xiang,Hui Liu,Yu Huang,You Zhou,Junhan Lu,Enze Zhou

doi:10.3389/fenrg.2022.829934

Abstract

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.

Highlights

The continuously growing economy has brought a higher demand for electricity in China in recent years
P xi yj ωi According to the wildfire risk assessment of the transmission corridor based on Naïve Bayes (NB) [16], the conditional probability of factors under fire or non-fire conditions is obtained
According to the importance rank of information gain ratio, some factors contribute little to the wildfire risk assessment

Summary

INTRODUCTION

The continuously growing economy has brought a higher demand for electricity in China in recent years. Fire-spot density factors combined with vegetation burning hazard levels are used to assess and classify the risk level of wildfires in the transmission-line corridors (QGDW11643, 2016). To accelerate the calculation efficiency of Bayes models, the data of all wildfire-related factors for both fire-spot and non-firespot samples are graded into four classes. Among the selected 14 wildfire-related factors, some of the factors may contribute little to the risk assessment and cause data redundancy, which increases the model complexity and decrease the accuracy. Vegetation type, land-usage type, and historical fire-spot density are the three most important factors in affecting the occurrence risk of wildfire. The DR and the aspect are the factors with the least influence on the wildfire occurrence

METHODOLOGY

RESULTS AND DISCUSSION

CONCLUSION