%0 Journal Article
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}
%@nexthigherunit 8JMKD3MGPCW/3ESGTTP 8JMKD3MGPCW/3ETL868
%@archivingpolicy denypublisher denyfinaldraft
%@secondarymark B3_ASTRONOMIA_/_FÍSICA B1_GEOCIÊNCIAS
%3 2009SW000532.pdf
%D 2010
%4 dpi.inpe.br/plutao@80/2010/06.25.16.28.54
%T Survey and prediction of the ionospheric scintillation using data mining techniques
%@usergroup administrator
%@usergroup lattes
%@usergroup marciana
%V 8
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%F lattes: 4555952412875598 1 RezendePaStKaMuSiCo:2010:SuPrIo
%@resumeid
%@resumeid
%@resumeid 8JMKD3MGP5W/3C9JJ9D
%@versiontype publisher
%X rregularly structured ionospheric regions may cause amplitude and phase fluctuations of radio signals. Such distortion is called ionospheric scintillation. These ionospheric irregularities occur as part of depleted plasma density regions that are generated at the magnetic equator after sunset by equatorial ionospheric plasma instability mechanism. Also known as ionospheric bubbles, they drift upward to high altitudes at the equator and extend/expand to low latitudes along the Earth magnetic field lines. Ionospheric irregularities affect the space weather since they present large variations with the solar cycle and during solar flares and coronal mass ejections. In general, navigation systems such as the Global Positioning System and telecommunications systems are also affected by the scintillation. The aim of this work is to apply data mining for the prediction of ionospheric scintillation. Data mining can be divided into two categories: descriptive or predictive. The first one describes a data set in a concise and summarized way, while the second one, used in this work, analyzes the data to build a model and tries to predict the behavior of a new data set. In this study we employed data series of ionospheric scintillation and other parameters such as the level of solar activity, vertical drift velocity of the plasma at the magnetic equator, and magnetic activity. The results show that prediction of the ionospheric scintillation occurrence during the analyzed period was possible regardless of the high variability of the ionospheric parameters that affect the generation of such irregularities.
%8 June
%@area COMP
%@secondarykey INPE--PRE/
%@electronicmailaddress luizfelipe@dae.inpe.br
%@documentstage not transferred
%K Data Mining, ionospheric scintillation, Bagging, GPS, Solar Cycle, prediction.
%@e-mailaddress luizfelipe@dae.inpe.br
%@doi 10.1029/2009SW000532
%@issn 1539-4956
%@group DAE-CEA-INPE-MCT-BR
%@group DAE-CEA-INPE-MCT-BR
%@group LAC-CTE-INPE-MCT-BR
%@group DAE-CEA-INPE-MCT-BR
%@dissemination WEBSCI; AGU.
%O Setores de Atividade: Atividades profissionais, científicas e técnicas, Outras atividades profissionais, científicas e técnicas.
%P S06D09
%A Rezende, Luiz Felipe Campos de,
%A Paula, E. R.,
%A Stephany, Stephan,
%A Kantor, I. J.,
%A Muella, M. T. A. H.,
%A Siqueira, P. M.,
%A Correa, K. S.,
%B Space Weather
%2 dpi.inpe.br/plutao@80/2010/06.25.16.28.55
%@secondarytype PRE PI