Matches in SemOpenAlex for { <https://semopenalex.org/work/W4382051297> ?p ?o ?g. }
Showing items 1 to 91 of
91
with 100 items per page.
- W4382051297 endingPage "3268" @default.
- W4382051297 startingPage "3251" @default.
- W4382051297 abstract "The integration of machine learning technology into ionospheric prediction has emerged as a burgeoning field of research. However, several challenges still exist, such as the disregard of delays in real-world data acquisition, variations in accuracy due to different input data and methods. In this study, we present an ionospheric Gaussian process regression (GPR) model with multiple input parameters to forecast global ionospheric total electron content (TEC). We assess two solutions based on GPR model that leverage different ionospheric data, namely the vertical TEC (VTEC) and spherical harmonic coefficients (SHC) prediction solutions. Our findings demonstrate that both GPR models exhibit strong accuracy and stability. Specifically, for 1-day-ahead-predicted global ionospheric maps (GIMs) in 2015 year and 2019 year, the root mean square error (RMSE) of the SHC prediction solution is 4.343 TECU and 1.702 TECU, respectively, while the VTEC prediction solution has RMSE values of 4.321 TECU and 1.673 TECU. Moreover, in high and low solar activity, over 29% and 68% of the absolute residuals are within 1.0 TECU, respectively. Furthermore, we compare the GPR model with conventional methods (such as Adaptive Autoregressive Model (AAR)) and observe that the RMSE of the GPR model is lower than that of the AAR model for GIMs predicted under different solar and geomagnetic activities, with a difference ranging from 0.2 to 0.4 TECU. In addition, the GPR model can provide prediction intervals for the predicted values. These intervals are typically represented using the mean and standard deviation, where the mean represents the predicted value and the standard deviation represents the uncertainty associated with the prediction. Prediction intervals can assist users in understanding the uncertainty of the model's predictions." @default.
- W4382051297 created "2023-06-27" @default.
- W4382051297 creator A5053998916 @default.
- W4382051297 creator A5089381554 @default.
- W4382051297 creator A5091782753 @default.
- W4382051297 date "2023-10-01" @default.
- W4382051297 modified "2023-10-06" @default.
- W4382051297 title "Predicting global ionospheric TEC maps using Gaussian process regression" @default.
- W4382051297 cites W1587976434 @default.
- W4382051297 cites W1974904413 @default.
- W4382051297 cites W2022032575 @default.
- W4382051297 cites W2022348995 @default.
- W4382051297 cites W2028038204 @default.
- W4382051297 cites W2060552391 @default.
- W4382051297 cites W2075056964 @default.
- W4382051297 cites W2086955951 @default.
- W4382051297 cites W2087523516 @default.
- W4382051297 cites W2090288797 @default.
- W4382051297 cites W2141234925 @default.
- W4382051297 cites W2148674477 @default.
- W4382051297 cites W2502503454 @default.
- W4382051297 cites W2594931141 @default.
- W4382051297 cites W2606268754 @default.
- W4382051297 cites W2770194917 @default.
- W4382051297 cites W2790261559 @default.
- W4382051297 cites W2792534869 @default.
- W4382051297 cites W2895220625 @default.
- W4382051297 cites W2908743908 @default.
- W4382051297 cites W3009289413 @default.
- W4382051297 cites W3009919705 @default.
- W4382051297 cites W3011954595 @default.
- W4382051297 cites W3022421877 @default.
- W4382051297 cites W3023463595 @default.
- W4382051297 cites W3093564076 @default.
- W4382051297 cites W3125566411 @default.
- W4382051297 cites W3145491624 @default.
- W4382051297 cites W3189164715 @default.
- W4382051297 cites W4251014520 @default.
- W4382051297 cites W4287448221 @default.
- W4382051297 cites W4293571976 @default.
- W4382051297 doi "https://doi.org/10.1016/j.asr.2023.06.036" @default.
- W4382051297 hasPublicationYear "2023" @default.
- W4382051297 type Work @default.
- W4382051297 citedByCount "1" @default.
- W4382051297 countsByYear W43820512972023 @default.
- W4382051297 crossrefType "journal-article" @default.
- W4382051297 hasAuthorship W4382051297A5053998916 @default.
- W4382051297 hasAuthorship W4382051297A5089381554 @default.
- W4382051297 hasAuthorship W4382051297A5091782753 @default.
- W4382051297 hasConcept C105795698 @default.
- W4382051297 hasConcept C116403925 @default.
- W4382051297 hasConcept C127313418 @default.
- W4382051297 hasConcept C139945424 @default.
- W4382051297 hasConcept C159877910 @default.
- W4382051297 hasConcept C165391973 @default.
- W4382051297 hasConcept C176379880 @default.
- W4382051297 hasConcept C22679943 @default.
- W4382051297 hasConcept C33923547 @default.
- W4382051297 hasConcept C8058405 @default.
- W4382051297 hasConcept C81692654 @default.
- W4382051297 hasConceptScore W4382051297C105795698 @default.
- W4382051297 hasConceptScore W4382051297C116403925 @default.
- W4382051297 hasConceptScore W4382051297C127313418 @default.
- W4382051297 hasConceptScore W4382051297C139945424 @default.
- W4382051297 hasConceptScore W4382051297C159877910 @default.
- W4382051297 hasConceptScore W4382051297C165391973 @default.
- W4382051297 hasConceptScore W4382051297C176379880 @default.
- W4382051297 hasConceptScore W4382051297C22679943 @default.
- W4382051297 hasConceptScore W4382051297C33923547 @default.
- W4382051297 hasConceptScore W4382051297C8058405 @default.
- W4382051297 hasConceptScore W4382051297C81692654 @default.
- W4382051297 hasIssue "8" @default.
- W4382051297 hasLocation W43820512971 @default.
- W4382051297 hasOpenAccess W4382051297 @default.
- W4382051297 hasPrimaryLocation W43820512971 @default.
- W4382051297 hasRelatedWork W1922044487 @default.
- W4382051297 hasRelatedWork W1989070650 @default.
- W4382051297 hasRelatedWork W2168737167 @default.
- W4382051297 hasRelatedWork W2536398560 @default.
- W4382051297 hasRelatedWork W2550010074 @default.
- W4382051297 hasRelatedWork W3012062545 @default.
- W4382051297 hasRelatedWork W3110665281 @default.
- W4382051297 hasRelatedWork W4308441248 @default.
- W4382051297 hasRelatedWork W4381849504 @default.
- W4382051297 hasRelatedWork W4382051297 @default.
- W4382051297 hasVolume "72" @default.
- W4382051297 isParatext "false" @default.
- W4382051297 isRetracted "false" @default.
- W4382051297 workType "article" @default.