Design of forest fire risk meteorological forecast model based on data superposition

Jian Wang; Lei Wang; Danchuang Zhang; Jianwen Xu; Mingshan Tang; Xueling Weng

doi:10.1117/12.2682507

26 May 2023 Design of forest fire risk meteorological forecast model based on data superposition

Jian Wang, Lei Wang, Danchuang Zhang, Jianwen Xu, Mingshan Tang, Xueling Weng

Proceedings Volume 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 127000E (2023) https://doi.org/10.1117/12.2682507
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China

Abstract

Using the data of automatic weather observation station, intelligent grid forecast, forest vegetation underlying surface, this paper selects meteorological factors such as temperature, precipitation, wind speed, relative humidity as dynamic factors, and the vegetation underlying surface data matching the intelligent grid based on geographic information as static factors. A 1km×1km refined forest fire weather grade forecast model based on data superposition was built, and GIS spatial analysis and visualization technology was applied to the construction of forest fire risk model and the production of fire risk meteorological grade forecast graphics. The business application of this research is of great significance to the establishment of a complete forest fire prevention system in Dalian, the protection of the natural environment of the forest, the maintenance of sustainable ecological development and the safety of human life and property.

Citation Download Citation

Jian Wang, Lei Wang, Danchuang Zhang, Jianwen Xu, Mingshan Tang, and Xueling Weng "Design of forest fire risk meteorological forecast model based on data superposition", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 127000E (26 May 2023); https://doi.org/10.1117/12.2682507

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