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dc.contributor.author
Kiani Shahvandi, Mostafa
dc.date.accessioned
2021-02-25T19:52:04Z
dc.date.available
2021-01-05T11:36:16Z
dc.date.available
2021-02-25T19:52:04Z
dc.date.issued
2020-05-22
dc.identifier.uri
http://hdl.handle.net/20.500.11850/459463
dc.description.abstract
In this paper, the generalized regression neural network is used to predict the GNSS position time series. Using the IGS 24-hour final solution data for Bad Hamburg permanent GNSS station in Germany, it is shown that the larger the training of the network, the higher the accuracy is, regardless of the time span of the time series. In order to analyze the performance of the neural network in various conditions, 14 permanent stations are used in different countries, namely, Spain, France, Romania, Poland, Russian Federation, United Kingdom, Czech Republic, Sweden, Ukraine, Italy, Finland, Slovak Republic, Cyprus, and Greece. The performance analysis is divided into two parts, continuous data-without gaps-and discontinuous ones-having intervals of gaps with no data available. Three measure of error are presented, namely, symmetric mean absolute percentage error, standard deviation, and mean of absolute errors. It is shown that for discontinuous data the position can be predicted with an accuracy of up to 6 centimeters, while the continuous data positions present a higher prediction accuracy, as high as 3 centimeters. In order to compare the results of this machine learning algorithm with the traditional statistical approaches, the Theta method is used, which is well-established for high-accuracy time series prediction. The comparison shows that the generalized regression neural network machine learning algorithm presents better accuracy than the Theta method, possibly up to 250 times. In addition, it is approximately 4.6 times faster.
en_US
dc.language.iso
en
en_US
dc.publisher
Cornell University
en_US
dc.title
On the suitability of generalized regression neural networks for GNSS position time series prediction for geodetic applications in geodesy and geophysics
en_US
dc.type
Working Paper
ethz.journal.title
arXiv
ethz.pages.start
2005.11106
en_US
ethz.size
17 p.
en_US
ethz.identifier.arxiv
2005.11106
ethz.publication.place
Ithaca, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02647 - Inst. f. Geodäsie und Photogrammetrie / Institute of Geodesy and Photogrammetry::09707 - Soja, Benedikt / Soja, Benedikt
en_US
ethz.date.deposited
2021-01-05T11:36:23Z
ethz.source
FORM
ethz.eth
no
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-02-25T19:52:17Z
ethz.rosetta.lastUpdated
2021-02-25T19:52:17Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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