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Cited by 295Climate variability and modelsMeteorological Phenomena and SimulationsForecasting Techniques and Applications

Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms

Ahmed, Kamal, Sachindra, D.A., Shahid, Shamsuddin, Iqbal, Zafar, Nawaz, Nadeem, Khan, Najeebullah·2020·Atmospheric Research
DOI 10.1016/j.atmosres.2019.104806
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Crossref · 10.1016/j.atmosres.2019.104806

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  • The 50% top-performing GCMs are optimum for climate change impact studies to reduce uncertainty in model projections.

    “It is suggested that the 50% top-performing GCMs are optimum for the climate change impact studies to reduce the uncertainty in the model projections (Ahmed et al. 2020;Raju & Nagesh Kumar 2020).”

    From Catchment response to climate change under CMIP6 scenarios: a case study of the Krishna River Basin (2024) · cites this paper as Ahmed et al. 2020

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References 84 · Cited by 295

Authors
Ahmed, K., Sachindra, D. A., Shahid, S., Iqbal, Z., Nawaz, N., & Khan, N.
Affiliations
  • Lasbela University of Agriculture Water and Marine Science (PK)
  • Victoria University (AU)
  • University of Technology Malaysia (MY)
Corresponding author
Ahmed, Kamal
ORCID
Venue
Atmospheric Research
Publisher
Elsevier BV
Published
2020-05
Volume
236
Pages
104806
ISSN
0169-8095 (ISSN-L)
DOI
10.1016/j.atmosres.2019.104806
OpenAlex
W2995378905
Cited by
295
References
84
Citations by year
2026: 41 · 2025: 64 · 2024: 67 · 2023: 46 · 2022: 32 · 2021: 31 · 2020: 14
FWCI
9.91
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Atmospheric Research (publishedVersion)
  • Victoria University Research Repository (Victoria University) (submittedVersion)
Funders
Universiti Teknologi Malaysia
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
Keywords
multi-model ensemble, climate model uncertainty, precipitation, temperature, relevance vector machine, k-nearest neighbors, support vector machine, artificial neural networks, CMIP5, monsoon, winter climate, Pakistan climate, HadGEM2-AO

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