Artificial intelligence-driven precipitation downscaling and projections over Thailand using CMIP6 climate models
Global warming has intensified the hydrological cycle, increased the frequency and severity of extreme precipitation events, and necessitated the collection of accurate future precipitation data for effective disaster mitigation and informed decision-making. The research evaluates the performance of...
Saved in:
| Main Authors: | Muhammad Waqas, Usa Wannasingha Humphries |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-08-01
|
| Series: | Big Earth Data |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/20964471.2025.2547500 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Integrating double techniques of statistical downscaling and bias correction to reduce bias in projections trends of future climate datasets
by: Amnah Alasqah, et al.
Published: (2025-08-01) -
Extreme gradient and boosting algorithm for improved bias-correction and downscaling of CMIP6 GCM data across indian river basin
by: Chandni Thakur, et al.
Published: (2025-06-01) -
Projected Future Changes in Tropical Cyclones Using the CMIP6 HighResMIP Multimodel Ensemble
by: Malcolm John Roberts, et al.
Published: (2020-07-01) -
Wave Downscaling Approach with TCN model, Case Study in Bengkulu, Indonesia
by: Dio Navialdy, et al.
Published: (2024-08-01) -
A dataset of large ensemble of CMIP6-based transient climate scenarios for impact assessment in Great BritainZenodo
by: Mikhail A. Semenov, et al.
Published: (2025-08-01)