CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12551 > Article
Paper
23 February 2023 Forecast of severe typhoon formation based on unbalanced data
Xiaotian Pan, Xiang Wang
Author Affiliations +
Proceedings Volume 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022); 125510C (2023) https://doi.org/10.1117/12.2668144
Event: Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 2022, Changchun, China
Abstract
As extreme weather events, severe typhoons have a great impact on people's daily production and social stability. Therefore, many scholars have used different methods to predict the formation of severe typhoons in order to mitigate the impact of severe typhoons. However, the severe typhoon in reality is a small probability event and the number of severe typhoon samples used for prediction is much smaller than the number of ordinary typhoon samples. Existing models rarely consider the impact of unbalanced data on model training and prediction, which makes it difficult to apply the model to the actual severe typhoon prediction. Therefore, we propose a severe typhoon formation prediction model based on unbalanced data. The model uses a convolutional neural network to obtain features from the severe typhoon environmental field and an LSTM model to implement the prediction of severe typhoon formation. A customized loss function designed in this paper is used to add different classification weights for normal typhoon samples and severe typhoon samples, so that the model improves the prediction of unbalanced severe typhoon formation. The experiments show that the severe typhoon formation prediction model based on unbalanced data outperforms the traditional machine learning model for unbalanced severe typhoon formation prediction.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Xiaotian Pan and Xiang Wang "Forecast of severe typhoon formation based on unbalanced data", Proc. SPIE 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 125510C (23 February 2023); https://doi.org/10.1117/12.2668144
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
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
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 6 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Data modeling
Machine learning
Feature extraction
Meteorology
Convolutional neural networks
Wind speed
Deep learning
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top