Missing data imputation of MAGDAS-9’s ground electromagnetism with supervised machine learning and conventional statistical analysis models

Missing data imputation of MAGDAS-9’s ground electromagnetism with supervised machine learning and conventional statistical analysis models

Data imputation studies include reconstruction or estimation of imperfect data gaps caused by system sensing failure, and non-responsive data transmission remains an open issue. In space weather applications, imputation of ground electromagnetism is significant in capturing the complex interaction o...

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Bibliographic Details
Main Authors: Akimasa, Y. (Author), H., M.A (Author), Huzaimy Jusoh, M. (Author), Iffah Abd Latiff, Z. (Author), K.A., N.D (Author), Md Tahir, N. (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2022
Series:Alexandria Engineering Journal
Subjects:
Benchmarking
Data gap
Errors
Geomagnetic storm
Geomagnetism
Imputation
Mean absolute error
Mean square error
Means square errors
Missing dataset
Neural networks
Performance
Space weather
Supervised learning
Supervised machine learning
Support vector regressions
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