Research on meteorological risk prediction method based on machine learning algorithm

Ping Li; Yu Chen; Shuo Liu; Li Tian; Jinglong Lu

doi:10.1117/12.2660125

3 February 2023 Research on meteorological risk prediction method based on machine learning algorithm

Ping Li, Yu Chen, Shuo Liu, Li Tian, Jinglong Lu

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125112C (2023) https://doi.org/10.1117/12.2660125
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

Among all kinds of natural disasters, meteorological disasters cause the greatest economic losses. Meteorological disasters have multiple causes. The characteristics of systematization, social amplification, unpredictability and urgency have caused various. The loss is incalculable. Therefore, the risk assessment and management of meteorological disasters is very important. So far, there is still no universally practical and systematic theory of meteorological disaster risk assessment and management. The research on meteorological disaster risk assessment and management in China started relatively late, Moreover, flood and drought are the main disasters, and there are few studies on other disasters, such as typhoons and hurricanes. Through the method of case analysis, this paper summarizes some internal, hoping to increase the research content in this field.

Citation Download Citation

Ping Li, Yu Chen, Shuo Liu, Li Tian, and Jinglong Lu "Research on meteorological risk prediction method based on machine learning algorithm", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125112C (3 February 2023); https://doi.org/10.1117/12.2660125

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available