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dc.contributor.author Fajardo, G.
dc.contributor.author Pacheco, Edgardo E.
dc.date.accessioned 2021-07-09T13:11:50Z
dc.date.available 2021-07-09T13:11:50Z
dc.date.issued 2021-06
dc.identifier.uri http://hdl.handle.net/20.500.12816/4963
dc.description Poster presented at the 2021 CEDAR Virtual Workshop, June 20-25.
dc.description.abstract The equatorial ionosphere has been extensively studied using purely physical models, however in recent years, with a large amount of data, it has been possible to improve these models using machine learning techniques. In this paper, we share the research results aimed to evaluate the influence of space weather parameters on GPS position approximation. We evaluated data from the Huancayo GPS station between 2016 and 2020 and we have taken into account the space weather data from the OMNI website, scintillation index (S4) and position data obtained from the GPS of the LISN network to perform our model. In addition, we use tropospheric conditions provided by the Geophysical Institute of Peru (IGP). The final result is a reliability matrix obtained with an XG Boost algorithm that will allow us to evaluate if a GPS signal given the conditions is indeed reliable or not. es_ES
dc.format application/pdf es_ES
dc.language.iso eng es_ES
dc.publisher Instituto Geofísico del Perú es_ES
dc.rights info:eu-repo/semantics/openAccess es_ES
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/ es_ES
dc.subject GNSS es_ES
dc.subject Machine learning es_ES
dc.subject Space weather es_ES
dc.title Exploration of machine learning tools developed for the study of space weather and its impact on position approximation in GNSS systems es_ES
dc.type info:eu-repo/semantics/conferenceObject es_ES
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#1.05.01 es_ES

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