Developing an ensemble machine learning framework for enhanced climate projections using CMIP6 data in the Middle East
Abstract Climate change in the Middle East has intensified with rising temperatures, shifting rainfall patterns, and more frequent extreme events. This study introduces the Stacking-EML framework, which merges five machine learning models three meta-learners to predict maximum temperature, minimum t...
Saved in:
| Main Authors: | Younes Khosravi, Taha B.M.J. Ouarda, Saeid Homayouni |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | npj Climate and Atmospheric Science |
| Online Access: | https://doi.org/10.1038/s41612-025-01033-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Spatio-temporal evaluation of MODIS temperature vegetation dryness index in the Middle East
by: Younes Khosravi, et al.
Published: (2024-12-01) -
Drought risks are projected to increase in the future in central and southern regions of the Middle East
by: Younes Khosravi, et al.
Published: (2025-05-01) -
PDSI_CMIP6: an ensemble CMIP6-projected self-calibrated Palmer drought severity index dataset
by: Jinghua Xiong, et al.
Published: (2025-08-01) -
CMIP6 multi-model ensemble projection of reference evapotranspiration using machine learning algorithms
by: Milad Nouri, et al.
Published: (2024-12-01) -
Time Variability Correction of CMIP6 Climate Change Projections
by: Y. Shao, et al.
Published: (2024-02-01)