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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/45H2925
Repositorysid.inpe.br/mtc-m21d/2021/10.01.14.47
Last Update2021:10.01.14.47.48 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21d/2021/10.01.14.47.48
Metadata Last Update2022:04.03.22.39.31 (UTC) administrator
DOI10.3390/atmos12091202
ISSN2073-4433
Citation KeyBenoitPetr:2021:EvF1Su
TitleEvaluation of f10.7, sunspot number and photon flux data for ionosphere tec modeling and prediction using machine learning techniques
Year2021
MonthSept.
Access Date2024, May 18
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size11469 KiB
2. Context
Author1 Benoit, Andres Gilberto Machado da Silva
2 Petry, Adriano
ORCID1
2 0000-0001-6422-479X
Group1
2 COESU-CGGO-INPE-MCTI-GOV-BR
Affiliation1 Universidade Federal de Santa Maria (UFSM)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 es.benoit@acad.ufsm.br
2 ano.petry@inpe.br
JournalAtmosphere
Volume12
Number9
Pagese1202
Secondary MarkB3_ENGENHARIAS_III B3_ENGENHARIAS_I B3_CIÊNCIAS_AMBIENTAIS B4_ENGENHARIAS_II B5_GEOCIÊNCIAS
History (UTC)2021-10-01 14:47:48 :: simone -> administrator ::
2021-10-01 14:47:50 :: administrator -> simone :: 2021
2021-10-01 14:48:11 :: simone -> administrator :: 2021
2022-04-03 22:39:31 :: administrator -> simone :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsIonosphere modeling
Machine learning
Regression model
Total electron content
AbstractConsidering the growing volumes and varieties of ionosphere data, it is expected that automation of analytical model building using modern technologies could lead to more accurate results. In this work, machine learning techniques are applied to ionospheric modeling and prediction using sun activity data. We propose Total Electron Content (TEC) spectral analysis, using discrete cosine transform (DCT) to evaluate the relation to the solar features F10.7, sunspot number and photon flux data. The ionosphere modeling procedure presented is based on the assessment of a six-year period (20142019) of data. Different multi-dimension regression models were considered in experiments, where each geographic location was independently evaluated using its DCT frequency components. The features correlation analysis has shown that 5-year data seem more adequate for training, while learning curves revealed overfitting for polynomial regression from the 4th to 7th degrees. A qualitative evaluation using reconstructed TEC maps indicated that the 3rd degree polynomial regression also seems inadequate. For the remaining models, it can be noted that there is seasonal variation in root-mean-square error (RMSE) clearly related to the equinox (lower error) and solstice (higher error) periods, which points to possible seasonal adjustment in modeling. Elastic Net regularization was also used to reduce global RMSE values down to 2.80 TECU for linear regression.
AreaCEA
Arrangementurlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGGO > Evaluation of f10.7,...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGP3W34T/45H2925
zipped data URLhttp://urlib.net/zip/8JMKD3MGP3W34T/45H2925
Languageen
Target Filebenoit_evaluation.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policyallowpublisher allowfinaldraft
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/46KUBT5
Citing Item Listsid.inpe.br/bibdigital/2022/04.03.22.35 5
DisseminationWEBSCI; PORTALCAPES; SCOPUS.
Host Collectionurlib.net/www/2021/06.04.03.40
6. Notes
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