Lightning-Terrain Association Mining Based on Improved Apriori Algorithm
Yuzhe Chen, Yu Wang, Yeqiang Deng, Haochen Zhang, Lei Lan, Xishan Wen · 2025
In this paper, the cloud-to-ground (CG) lightning data and topography data in a region of China are collected and processed respectively, and the variation trend of CG lightning density and CG lightning strength with single topography factor is quantitatively analyzed. Then the Apriori association rule mining algorithm is improved to identify the typical terrain scene of lightning activity, and the operation efficiency is improved. It is found that the confidence of the association rules between CG lightning strength and topography is up to 75.07%, which shows obvious correlation. Finally, three typical terrain feature scenarios of strong lightning current are identified and summarized, including 1) high-altitude forest areas ($>605 ~\mathrm{m}$) on gentle slopes (3.19-10.04${}^{\circ}$), 2) high-altitude forests ($>605 ~\mathrm{m}$) with south, southwest, west, or northwest slope orientations, and 3) elevated forested zones ($>605 ~\mathrm{m}$) on windward slopes near valleys. Their causes are summarized, related to the high altitude atmospheric pressure reduction, the towering objects in flat and open areas triggering lightning discharges, and the local airflow in the valley. Through the research of this paper, the distribution law of lightning current and other parameters can be mastered, which can provide some reference for reducing the lightning risk of power system.