Generative Adversarial Network for Real‐Time Flash Drought Monitoring: A Deep Learning Study
Abstract Droughts are among the most devastating natural hazards, occurring in all regions with different climate conditions. The impacts of droughts result in significant damages annually around the world. While drought is generally described as a slow‐developing hazardous event, a rapidly developi...
Saved in:
| Main Authors: | Ehsan Foroumandi, Keyhan Gavahi, Hamid Moradkhani |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-05-01
|
| Series: | Water Resources Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2023WR035600 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Multivariate Evaluation of Flash Drought Across the United States
by: Jason A. Otkin, et al.
Published: (2024-11-01) -
Enhanced flash droughts in recent decades over the Arabian Peninsula
by: Md Saquib Saharwardi, et al.
Published: (2025-10-01) -
Machine‐Learning Based Multi‐Layer Soil Moisture Forecasts—An Application Case Study of the Montana 2017 Flash Drought
by: J. Du, et al.
Published: (2024-10-01) -
Increasing Trends in Soil Heat Extremes Following Flash Drought Outbreaks
by: Yangyang Jing, et al.
Published: (2025-08-01) -
Response and resilience of farmland ecosystems to flash drought in China
by: Yuanxin Dai, et al.
Published: (2025-07-01)