Details
| Original language | English |
|---|---|
| Pages (from-to) | 247-258 |
| Number of pages | 12 |
| Journal | Journal of geodesy |
| Volume | 76 |
| Issue number | 5 |
| Publication status | Published - May 2002 |
| Externally published | Yes |
Abstract
Earth orientation parameters (EOPs) [polar motion and length of day (LOD), or UTI-UTC] were predicted by artificial neural networks. EOP series from various sources, e.g. the C04 series from the International Earth Rotation Service and the re-analysis optical astrometry series based on the HIPPARCOS frame, served for training the neural network for both short-term and long-term predictions. At first, all effects which can be described by functional models, e.g. effects of the solid Earth tides and the ocean tides or seasonal atmospheric variations of the EOPs, were removed. Only the differences between the modeled and the observed EOPs, i.e. the quasi-periodic and irregular variations, were used for training and prediction. The Stuttgart neural network simulator, which is a very powerful software tool developed at the University of Stuttgart, was applied to construct and to validate different types of neural networks in order to find the optimal topology of the net, the most economical learning algorithm and the best procedure to feed the net with data patterns. The results of the prediction were analyzed and compared with those obtained by other methods. The accuracy of the prediction is equal to or even better than that by other prediction methods.
Keywords
- Earth rotation, Neural networks, Prediction
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Geophysics
- Earth and Planetary Sciences(all)
- Geochemistry and Petrology
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
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In: Journal of geodesy, Vol. 76, No. 5, 05.2002, p. 247-258.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Prediction of Earth orientation parameters by artificial neural networks
AU - Schuh, H.
AU - Ulrich, M.
AU - Egger, D.
AU - Müller, J.
AU - Schwegmann, W.
PY - 2002/5
Y1 - 2002/5
N2 - Earth orientation parameters (EOPs) [polar motion and length of day (LOD), or UTI-UTC] were predicted by artificial neural networks. EOP series from various sources, e.g. the C04 series from the International Earth Rotation Service and the re-analysis optical astrometry series based on the HIPPARCOS frame, served for training the neural network for both short-term and long-term predictions. At first, all effects which can be described by functional models, e.g. effects of the solid Earth tides and the ocean tides or seasonal atmospheric variations of the EOPs, were removed. Only the differences between the modeled and the observed EOPs, i.e. the quasi-periodic and irregular variations, were used for training and prediction. The Stuttgart neural network simulator, which is a very powerful software tool developed at the University of Stuttgart, was applied to construct and to validate different types of neural networks in order to find the optimal topology of the net, the most economical learning algorithm and the best procedure to feed the net with data patterns. The results of the prediction were analyzed and compared with those obtained by other methods. The accuracy of the prediction is equal to or even better than that by other prediction methods.
AB - Earth orientation parameters (EOPs) [polar motion and length of day (LOD), or UTI-UTC] were predicted by artificial neural networks. EOP series from various sources, e.g. the C04 series from the International Earth Rotation Service and the re-analysis optical astrometry series based on the HIPPARCOS frame, served for training the neural network for both short-term and long-term predictions. At first, all effects which can be described by functional models, e.g. effects of the solid Earth tides and the ocean tides or seasonal atmospheric variations of the EOPs, were removed. Only the differences between the modeled and the observed EOPs, i.e. the quasi-periodic and irregular variations, were used for training and prediction. The Stuttgart neural network simulator, which is a very powerful software tool developed at the University of Stuttgart, was applied to construct and to validate different types of neural networks in order to find the optimal topology of the net, the most economical learning algorithm and the best procedure to feed the net with data patterns. The results of the prediction were analyzed and compared with those obtained by other methods. The accuracy of the prediction is equal to or even better than that by other prediction methods.
KW - Earth rotation
KW - Neural networks
KW - Prediction
UR - http://www.scopus.com/inward/record.url?scp=0036081815&partnerID=8YFLogxK
U2 - 10.1007/s00190-001-0242-5
DO - 10.1007/s00190-001-0242-5
M3 - Article
AN - SCOPUS:0036081815
VL - 76
SP - 247
EP - 258
JO - Journal of geodesy
JF - Journal of geodesy
SN - 0949-7714
IS - 5
ER -