Jianqiao Sheng¹, Mengzhu Xu², Jin Han^3,*, Xingyan Deng²

Journal on Big Data, Vol.3, No.4, pp. 183-190, 2021, DOI:10.32604/jbd.2021.024892

Abstract Lightning disaster risk assessment, as an intuitive method to reflect the risk of regional lightning disasters, has aroused the research interest of many researchers. Nowadays, there are many schemes for lightning disaster risk assessment, but there are also some shortcomings, such as the resolution of the assessment is not clear enough, the accuracy rate cannot be verified, and the weight distribution has a strong subjective trend. This paper is guided by lightning disaster data and combines lightning data, population data and GDP data. Through support vector machine (SVM), it explores a way to combine artificial intelligence algorithms with lightning disaster… More >

Yan Zhang^1,2, Jin Han^1,2,*, Chengsheng Yuan^1,2, Shuo Yang³, Chuanlong Li^1,2, Xingming Sun^1,2

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 563-574, 2021, DOI:10.32604/cmc.2020.012502

Abstract Over the past 10 years, lightning disaster has caused a large number of casualties and considerable economic loss worldwide. Lightning poses a huge threat to various industries. In an attempt to reduce the risk of lightning-caused disaster, many scholars have carried out in-depth research on lightning. However, these studies focus primarily on the lightning itself and other meteorological elements are ignored. In addition, the methods for assessing the risk of lightning disaster fail to give detailed attention to regional features (lightning disaster risk). This paper proposes a grid-based risk assessment method based on data from multiple sources. First, this paper… More >