Advanced Meteorological Hazard Defense Capability Assessment: Addressing Sample Imbalance with Deep Learning Approaches

Advanced Meteorological Hazard Defense Capability Assessment: Addressing Sample Imbalance with Deep Learning Approaches

With the rise in meteorological disasters, improving evaluation strategies for disaster response agencies is critical. This shift from expert scoring to data-driven approaches is challenged by sample imbalance in the data, affecting accurate capability assessments. This study proposes a solution int...

Full description

Bibliographic Details
Main Authors: Jiansong Tang, Ryosuke Saga, Qiangsheng Dai, Yingchi Mao
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Applied Sciences
Subjects:
meteorological hazard defense
sample imbalance
loss function
classification
Online Access:https://www.mdpi.com/2076-3417/13/23/12561

Internet

https://www.mdpi.com/2076-3417/13/23/12561

Similar Items