loading page

Machine-Learning Research in the Space Weather Journal: Prospects, Scope and Limitations
  • +1
  • Noé Lugaz,
  • Huixin Liu,
  • Mike Hapgood,
  • Steven Morley
Noé Lugaz
University of New Hampshire

Corresponding Author:[email protected]

Author Profile
Huixin Liu
Kyushu University
Author Profile
Mike Hapgood
Rutherford Appleton Library
Author Profile
Steven Morley
Los Alamos National Lab
Author Profile


Manuscripts based on machine-learning techniques have significantly increased in Space Weather over the past few years. We discuss which manuscripts are within the journal’s scope and emphasize that manuscripts fo-cusing purely on a forecasting technique (rather than on understanding and forecasting a phenomenon) must correspond to a substantial improvement over the current state-of-the-art techniques and present this comparison. All manuscripts shall include information about data preparation, including splitting of data between training, validation and testing sets. The software and/or algorithms used for to develop the machine-learning technique should be included in a repository at the time of submission. Comparison with published results using other methods must be presented, and uncertainties of the forecast results must be discussed.
Dec 2021Published in Space Weather volume 19 issue 12. 10.1029/2021SW003000