Development of multi-model ensembles using tree-based machine learning methods to assess the future renewable energy potential: case of the East Thrace, Turkey-Reference-Cited by-同舟云学术

Development of multi-model ensembles using tree-based machine learning methods to assess the future renewable energy potential: case of the East Thrace, Turkey

Published:2023-07-08 Issue:37 Volume:30 Page:87314-87329
ISSN:1614-7499
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Guven Denizhan^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s11356-023-28649-9.pdf

Reference59 articles.

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2. Ahmed K, Sachindra DA, Shahid S, Demirel MC, Chung ES (2019) Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics. Hydrol Earth Syst Sci 23(11):4803–4824. https://doi.org/10.5194/hess-23-4803-2019

3. Ahmed K, Sachindra DA, Shahid S, Iqbal Z, Nawaz N, Khan N (2020) Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms. Atmos Res 236:104806. https://doi.org/10.1016/j.atmosres.2019.104806

4. Ahsan S, Bhat MS, Alam A, Farooq H, Shiekh HA (2022) Complementary use of multi-model climate ensemble and Bayesian model averaging for projecting river hydrology in the Himalaya. Environ Sci Pollut Res 1–23. https://doi.org/10.1007/s11356-022-24913-6

5. Akinsanola AA, Zhou W (2019) Projections of West African summer monsoon rainfall extremes from two CORDEX models. Clim Dyn 52(3):2017–2028. https://doi.org/10.1007/s00382-018-4238-8

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