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Cited by 10Solar and Space Plasma DynamicsSolar Radiation and PhotovoltaicsStock Market Forecasting Methods

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Novel residual hybrid machine learning for solar activity prediction in smart cities

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Abdulkadir, Rabiu Aliyu, Hasan, Mohammad Kamrul, Islam, Shayla, Gadekallu, Thippa Reddy, Pandey, Bishwajeet, Safie, Nurhizam, Syväjärvi, Mikael, Nasor, Mohamed·2023·Earth Science Informatics
DOI 10.1007/s12145-023-01130-4

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Metadata

Authors
Abdulkadir, R. A., Hasan, M. K., Islam, S., Gadekallu, T. R., Pandey, B., Safie, N., Syväjärvi, M., & Nasor, M.
Affiliations
  • National University of Malaysia (MY)
  • UCSI University (MY)
  • Lovely Professional University (IN)
  • Texas A&M University-Victoria (US)
  • Ajman University (AE)
Corresponding author
  • Hasan, Mohammad Kamrul
  • Islam, Shayla
ORCID
Venue
Earth Science Informatics
Publisher
Springer Science and Business Media LLC
Published
2023-11-01
Volume
16
Issue
4
Pages
3931-3945
ISSN
  • 1865-0473 (ISSN-L)
  • 1865-0481
DOI
10.1007/s12145-023-01130-4
OpenAlex
W4388134667
Cited by
10
References
2
Citations by year
2026: 3 · 2025: 4 · 2024: 3
FWCI
1.17
Type
journal article
Language
en
Open access
Licence
Locations
  • Earth Science Informatics (publishedVersion)
  • Research Square (acceptedVersion, cc-by)
Funders
  • Universiti Kebangsaan Malaysia (DIP 2022-021)
  • Swedish Energy Agency and and European Commission (775970)
Topics
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Solar and Space Plasma Dynamics
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Solar Radiation and Photovoltaics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
Keywords
solar activity forecasting, hybrid machine learning, support vector machine, long short-term memory neural network, linear regression, smart cities, space weather, computational complexity reduction

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