Matches in SemOpenAlex for { <https://semopenalex.org/work/W4308202694> ?p ?o ?g. }
Showing items 1 to 96 of
96
with 100 items per page.
- W4308202694 endingPage "2836" @default.
- W4308202694 startingPage "2827" @default.
- W4308202694 abstract "Ionospheric Total Electron Content (TEC) predominantly affects the radio wave communication and navigation links of Global Navigation Satellite Systems (GNSS). The ionospheric TEC exhibits a complex spatial–temporal pattern over equatorial and low latitude regions, which are difficult to predict for providing early warning alerts to GNSS users. Machine Learning (ML) techniques are proven better for ionospheric space weather predictions due to their ability of processing and learning from the available datasets of solar-geophysical data. Hence, a supervised ML algorithm such as the Support Vector Regression (SVR) model is proposed to predict TEC over northern equatorial and low latitudinal GNSS stations. The vertical TEC data estimated from GPS measurements for the entire 24th solar cycle period, 11 years (2009–2019), is considered over Bengaluru and Hyderabad International GNSS Service (IGS) stations. The performance of the proposed SVR model with kernel Gaussian or Radial Basis Function (RBF) is evaluated over the two selected testing periods during the High Solar Activity (HSA) year, 2014 and the Low Solar Activity (LSA) year, 2019. The proposed model performance is compared with Neural Networks (NN) model, and International Reference Ionosphere (IRI-2016) model during both LSA and HSA periods. It is noticed that the proposed SVR model has well predicted the VTEC values better than NN and IRI-2016 models. The experimental results of the SVR model evidenced that it could be an effective tool for predicting TEC over low-latitude and equatorial regions." @default.
- W4308202694 created "2022-11-09" @default.
- W4308202694 creator A5006912906 @default.
- W4308202694 creator A5037052967 @default.
- W4308202694 creator A5046497834 @default.
- W4308202694 date "2022-11-04" @default.
- W4308202694 modified "2023-09-26" @default.
- W4308202694 title "Support Vector Regression model to predict TEC for GNSS signals" @default.
- W4308202694 cites W11812926 @default.
- W4308202694 cites W1579429352 @default.
- W4308202694 cites W1586779072 @default.
- W4308202694 cites W1616020192 @default.
- W4308202694 cites W1922044487 @default.
- W4308202694 cites W1984592377 @default.
- W4308202694 cites W2011630059 @default.
- W4308202694 cites W2073545293 @default.
- W4308202694 cites W2156909104 @default.
- W4308202694 cites W2163545874 @default.
- W4308202694 cites W2322442010 @default.
- W4308202694 cites W2588308723 @default.
- W4308202694 cites W2807476577 @default.
- W4308202694 cites W2894278939 @default.
- W4308202694 cites W2912062348 @default.
- W4308202694 cites W3009289413 @default.
- W4308202694 cites W3043176619 @default.
- W4308202694 cites W3085336950 @default.
- W4308202694 doi "https://doi.org/10.1007/s11600-022-00954-w" @default.
- W4308202694 hasPublicationYear "2022" @default.
- W4308202694 type Work @default.
- W4308202694 citedByCount "2" @default.
- W4308202694 countsByYear W43082026942023 @default.
- W4308202694 crossrefType "journal-article" @default.
- W4308202694 hasAuthorship W4308202694A5006912906 @default.
- W4308202694 hasAuthorship W4308202694A5037052967 @default.
- W4308202694 hasAuthorship W4308202694A5046497834 @default.
- W4308202694 hasBestOaLocation W43082026941 @default.
- W4308202694 hasConcept C116403925 @default.
- W4308202694 hasConcept C12267149 @default.
- W4308202694 hasConcept C127313418 @default.
- W4308202694 hasConcept C127413603 @default.
- W4308202694 hasConcept C14279187 @default.
- W4308202694 hasConcept C146978453 @default.
- W4308202694 hasConcept C151325931 @default.
- W4308202694 hasConcept C153294291 @default.
- W4308202694 hasConcept C154945302 @default.
- W4308202694 hasConcept C165391973 @default.
- W4308202694 hasConcept C176379880 @default.
- W4308202694 hasConcept C19269812 @default.
- W4308202694 hasConcept C205649164 @default.
- W4308202694 hasConcept C2777966019 @default.
- W4308202694 hasConcept C2778027091 @default.
- W4308202694 hasConcept C41008148 @default.
- W4308202694 hasConcept C60229501 @default.
- W4308202694 hasConcept C62649853 @default.
- W4308202694 hasConcept C76155785 @default.
- W4308202694 hasConcept C8058405 @default.
- W4308202694 hasConceptScore W4308202694C116403925 @default.
- W4308202694 hasConceptScore W4308202694C12267149 @default.
- W4308202694 hasConceptScore W4308202694C127313418 @default.
- W4308202694 hasConceptScore W4308202694C127413603 @default.
- W4308202694 hasConceptScore W4308202694C14279187 @default.
- W4308202694 hasConceptScore W4308202694C146978453 @default.
- W4308202694 hasConceptScore W4308202694C151325931 @default.
- W4308202694 hasConceptScore W4308202694C153294291 @default.
- W4308202694 hasConceptScore W4308202694C154945302 @default.
- W4308202694 hasConceptScore W4308202694C165391973 @default.
- W4308202694 hasConceptScore W4308202694C176379880 @default.
- W4308202694 hasConceptScore W4308202694C19269812 @default.
- W4308202694 hasConceptScore W4308202694C205649164 @default.
- W4308202694 hasConceptScore W4308202694C2777966019 @default.
- W4308202694 hasConceptScore W4308202694C2778027091 @default.
- W4308202694 hasConceptScore W4308202694C41008148 @default.
- W4308202694 hasConceptScore W4308202694C60229501 @default.
- W4308202694 hasConceptScore W4308202694C62649853 @default.
- W4308202694 hasConceptScore W4308202694C76155785 @default.
- W4308202694 hasConceptScore W4308202694C8058405 @default.
- W4308202694 hasIssue "6" @default.
- W4308202694 hasLocation W43082026941 @default.
- W4308202694 hasOpenAccess W4308202694 @default.
- W4308202694 hasPrimaryLocation W43082026941 @default.
- W4308202694 hasRelatedWork W1631751207 @default.
- W4308202694 hasRelatedWork W2032777622 @default.
- W4308202694 hasRelatedWork W2761519948 @default.
- W4308202694 hasRelatedWork W2796783618 @default.
- W4308202694 hasRelatedWork W3190480360 @default.
- W4308202694 hasRelatedWork W3212236925 @default.
- W4308202694 hasRelatedWork W4210563673 @default.
- W4308202694 hasRelatedWork W4232096923 @default.
- W4308202694 hasRelatedWork W4293098304 @default.
- W4308202694 hasRelatedWork W80163176 @default.
- W4308202694 hasVolume "70" @default.
- W4308202694 isParatext "false" @default.
- W4308202694 isRetracted "false" @default.
- W4308202694 workType "article" @default.