Detection of GNSS-TEC Noise Related to the Tonga Volcanic Eruption Using Optimization Machine Learning Techniques and Integrated Data

dc.contributor.authorLe, Nhung
dc.contributor.authorMännel, Benjamin
dc.contributor.authorBui, Luyen K.
dc.contributor.authorJarema, Mihaela
dc.contributor.authorNguyen, Thai Chinh
dc.contributor.authorSchuh, Harald
dc.date.accessioned2023-09-12T11:42:04Z
dc.date.available2023-09-12T11:42:04Z
dc.date.issued2023-02-25
dc.description.abstractTotal Electron Content (TEC) is the integral of the electron density along the path between receivers and satellites. TEC measured from Global Navigation Satellite Systems (GNSS) data is valuable to monitor space weather and correct ionospheric models. TEC noise detection is also an essential channel to forecast space weather and research the relationship between the atmosphere and natural phenomena like geomagnetic storms, earthquakes, volcanos, and tsunamis. In this study, we apply optimization machine learning techniques and integrated GNSS and solar activity data to determine GNSS-TEC noise at the International GNSS Service (IGS) stations in the Tonga volcanic region. We investigate 38 indices related to the geomagnetic field and solar wind plasma to select the essential parameters for forecast models. The findings show the best-suited parameters to predict vertical TEC time series: plasma temperature (or Plasma speed), proton density, Lyman alpha, R sunspot, Ap index (or Kp, Dst), and F10.7 index. Applying the Ensemble algorithm to build the TEC forecast models at the investigated IGS stations gets the accuracy from 1.01 to 3.17 TECU. The study also shows that machine learning combined with integrated data can provide a robust approach to detecting TEC noise caused by seismic activities.en
dc.identifier.eissn1863-5539
dc.identifier.isbn9783031204623
dc.identifier.isbn9783031204630
dc.identifier.issn1863-5520
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/20009
dc.identifier.urihttps://doi.org/10.14279/depositonce-18807
dc.language.isoen
dc.publisherSpringer International Publishing
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subject.ddc500 Naturwissenschaften und Mathematik::550 Geowissenschaften, Geologie::550 Geowissenschaften
dc.subject.othermachine learningen
dc.subject.otherGNSS-TEC forecasten
dc.subject.otherGNSSen
dc.subject.othersolar activityen
dc.subject.otherTonga volcanic eruptionen
dc.titleDetection of GNSS-TEC Noise Related to the Tonga Volcanic Eruption Using Optimization Machine Learning Techniques and Integrated Data
dc.typeConference Object
dc.type.versionacceptedVersion
dcterms.bibliographicCitation.doi10.1007/978-3-031-20463-0_9
dcterms.bibliographicCitation.editorSpringer
dcterms.bibliographicCitation.originalpublishernameSpringer Nature
dcterms.bibliographicCitation.originalpublisherplaceHeidelberg
dcterms.bibliographicCitation.pageend157
dcterms.bibliographicCitation.pagestart137
dcterms.bibliographicCitation.proceedingstitleAdvances in Geospatial Technology in Mining and Earth Sciences
dcterms.rightsHolder.referenceVerlagspolicy
dcterms.rightsHolder.urlhttps://web.archive.org/web/20230726105531/https://www.springernature.com/gp/open-research/policies/book-policies
tub.accessrights.dnbdomain*
tub.affiliationFak. 6 Planen Bauen Umwelt::Inst. Geodäsie und Geoinformationstechnik::FG Satellitengeodäsie
tub.publisher.universityorinstitutionTechnische Universität Berlin

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