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Interpolation and extrapolation techniques based Neural Network in estimating the missing ionospheric TEC data

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dc.Conferencecode124897
dc.Conferencedate8 August 2016 through 11 August 2016
dc.Conferencename2016 Progress In Electromagnetics Research Symposium, PIERS 2016
dc.citedby1
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.authorJayapal V.en_US
dc.contributor.authorZain A.F.M.en_US
dc.date.accessioned2024-05-28T08:46:00Z
dc.date.available2024-05-28T08:46:00Z
dc.date.issued2016
dc.description.abstractThis paper investigates the capabilities of Neural Network (NN) application in estimating the missing ionospheric total electron content (TEC) data via interpolation and extrapolation methods. TEC is an important parameter that describes the state of the ionosphere. A multilayer feed-forward network with a back propagation algorithm is applied to estimate the TEC over Parit Raja (Lat. 1�52?N, Long. 103�06?E) an equatorial latitude station in Malaysia. The solar and magnetic indices, seasonal variation as well as diurnal variation are used as the input spaces in the NN to estimate the missing GPS TEC. The studies period is based on short term data during the medium solar activity period from 2005 to 2006. Normalized RMSE, RMSE and relative correction are computed for both methods to evaluate the capability of NN to interpolate and extrapolate the missing data. The results show that NN is able to interpolate the missing TEC data within the input range better than extrapolating the missing data outside the input range. The NN2 model finds hard to extrapolate the missing TEC data especially during the night hours, where the GPS TEC values are underestimated. Overall the relative correction of NN1 model is above 90% while for NN2 is below 90% for all ranges of missing rate. � 2016 IEEE.
dc.description.natureFinalen_US
dc.identifier.ArtNo7734425
dc.identifier.doi10.1109/PIERS.2016.7734425
dc.identifier.epage699
dc.identifier.isbn9781510000000
dc.identifier.scopus2-s2.0-85006711890
dc.identifier.spage695
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85006711890&doi=10.1109%2fPIERS.2016.7734425&partnerID=40&md5=8cb596864385cf710730b6d06aa6d7ac
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/9424
dc.languageEnglish
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2016 Progress In Electromagnetics Research Symposium, PIERS 2016 - Proceedings
dc.sourceScopus
dc.subjectBackpropagationen_US
dc.subjectBackpropagation algorithmsen_US
dc.subjectInterpolationen_US
dc.subjectIonosphereen_US
dc.subjectSolar energyen_US
dc.subjectDiurnal variationen_US
dc.subjectExtrapolation methodsen_US
dc.subjectExtrapolation techniquesen_US
dc.subjectIonospheric total electron contenten_US
dc.subjectMagnetic indicesen_US
dc.subjectMulti-layer feed-forward networksen_US
dc.subjectNeural network (nn)en_US
dc.subjectSeasonal variationen_US
dc.subjectExtrapolationen_US
dc.titleInterpolation and extrapolation techniques based Neural Network in estimating the missing ionospheric TEC data
dc.typeConference Paperen_US
dspace.entity.typePublication

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