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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/578198
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dc.contributor.authorRohaida Mat Akir (UKM)
dc.contributor.authorKalaivani Chellappan (UKM)
dc.contributor.authorMardina Abdullah (UKM)
dc.date.accessioned2023-11-06T02:59:07Z-
dc.date.available2023-11-06T02:59:07Z-
dc.date.issued2017-06
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:113380
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/578198-
dc.descriptionSpace weather forecasting and its importance for the power and communication industry have inspired research related to TEC forecasting lately. Research has attempted to establish an empirical model approach for TEC prediction. In this paper, artificial neural networks (ANNs) have been applied in total electron content using GPS Ionospheric Scintillation and TEC Monitor (GISTM) data from UKM Station. The TEC prediction will be useful in improving the quality of current GNSS applications, such as in automobiles, road mapping, location-based advertising, personal navigation or logistics. Hence, a neural network model was designed with relevant features and customised parameters. Various types of input data and data representations from the ionospheric activity were used for the chosen network structure, which was a three-layer perceptron trained by feed forward back propagation method and tested on the chosen test data. We found that the optimum RMSE occurred with 10 nodes as the best NN for GISTM UKM station for the studied period with RMSE 1.3457 TECU. An analysis was made to compare the TEC from the measured TEC with neural network prediction and from IRI-corr model. The results showed that the NN model forecast the TEC values close to the measured TEC values with 9.96% of relative error. Thus, the forecasting of total electron content has the potential to be implemented successfully with larger data set from multi-centred environment.
dc.language.isoen
dc.publisherUniversiti Putra Malaysia Press
dc.relation.haspartPertanika Journals
dc.relation.urihttp://www.pertanika.upm.edu.my/view_archives.php?journal=JST-25-S-6
dc.rightsUKM
dc.subjectForecasting
dc.subjectGPS
dc.subjectIRI-corr model
dc.subjectNeural network
dc.subjectTotal electron content
dc.titleTotal electron content forecasting using artificial neural network
dc.typeJournal Article
dc.format.volume25
dc.format.pages19-28
dc.format.issueSpecial Issue
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