@Article{jbd.2021.024892,
AUTHOR = {Jianqiao Sheng, Mengzhu Xu, Jin Han, Xingyan Deng},
TITLE = {A Lightning Disaster Risk Assessment Model Based on SVM},
JOURNAL = {Journal on Big Data},
VOLUME = {3},
YEAR = {2021},
NUMBER = {4},
PAGES = {183--190},
URL = {http://www.techscience.com/jbd/v3n4/46037},
ISSN = {2579-0056},
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 risk assessment. By fitting the lightning disaster 
data, the weight distribution between the various impact factors is obtained. In the 
experiment, the probability of lightning disaster is used to compare with the actual 
occurrence of lightning disaster. It can be found that the disaster risk assessment 
model proposed in this paper is more reasonable for the lightning risk. It has been 
verified that the accuracy rate of the assessment model in this paper has reached 
80.2%, which reflects the superiority of the model.},
DOI = {10.32604/jbd.2021.024892}
}