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Estimation of precipitable water vapor using least squares support vector regression and comparison with other models
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Maryam Cheginin   , Behzad Voosoghi , Seyyed Reza Ghaffari-Razin * |
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Abstract: (500 Views) |
The LS-SVR model uses simple linear equations in the training phase. As a result, the complexity of the computational algorithm is reduced; the speed of convergence and the accuracy of the results are increased. Seven parameters of longitude and latitude of GPS station, day of year (DOY), time to universal time (UT), relative humidity (RH), temperature (T) and pressure (P) are considered as inputs of LS-SVR model. And the PWV corresponding to these seven parameters is the output of the model. After the training step, the PWV value was estimated with the trained model and compared with the PWV values obtained from the radiosonde station, the empirical model of Saastamoinen and GPT3, the support vector regression model (SVR) and the radial basis neural network model (RBNN) in the control stations. Statistical indices of relative error, correlation coefficient and root mean square error (RMSE) have been used to evaluate the accuracy of the models. The conducted analyzes show that the average RMSE of RBNN, SVR, LS-SVR, GPT3 and Saastamoinen models in 3 control stations is to 4.92, 4.13, 2.87, 4.22 and 4.29 mm, respectively. Also, the average relative error of the models in 3 control stations is calculated as 38.06, 30.77, 22.37, 34.63 and 32.80% respectively. Analysis of the PPP method shows an improvement of 33 mm in the coordinate components using the LS-SVR model. The results of this thesis show that the LS-SVR model can be considered as an alternative to the empirical troposphere models in the studied area. The LS-SVR model is a local troposphere model with high accuracy.
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Article number: 2 |
| Keywords: troposphere, PWV, GPS, machine learning, LS-SVR. |
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Full-Text [PDF 941 kb]
(173 Downloads)
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Type of Study: Research |
Subject:
Geo&Hydro
Received: 2023/07/29
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1. Ghaffari Razin, M.R., Voosoghi, B., (2020). "Estimation of tropospheric wet refractivity using tomography method and artificial neural networks in Iranian case study". GPS Solutions 24(3):1-14. [ DOI:10.1007/s10291-020-00979-y] 2. Adavi, Z., Mashhadi-Hossainali, M., (2015). "4D-tomographic reconstruction of water vapor using the hybrid regularization technique with application to the North West of Iran". Advances in Space Research, 55(7):1845-1854. [ DOI:10.1016/j.asr.2015.01.025] 3. Haji Aghajany, S., Amerian, Y., (2017). "Three dimensional ray tracing technique for tropospheric water vapor tomography using GPS measurements". Journal of Atmospheric and Solar-Terrestrial Physics, 164 (2017):81-88. [ DOI:10.1016/j.jastp.2017.08.003] 4. Zhao, Q., Du, Z., Yao, W., Yao, Y., (2020). "Hybrid precipitable water vapor fusion model in China". Journal of Atmospheric and Solar-Terrestrial Physics. 208,
https://doi.org/10.1016/j.jastp.2020.105387 [ DOI:10.1016/j.jastp.2020.105387.] 5. Xia, P., Ye, S., Jiang, P., Pan, L., and Guo, M., (2018). "Assessing water vapor tomography in Hong Kong with improved vertical and horizontal constraints". In Proceedings Annales Geophysicae2018, Volume 36, Copernicus Publications Göttingen, Germany, p. 969-978. [ DOI:10.5194/angeo-36-969-2018] 6. Yang, F., Sun, Y., Meng, X., Guo, J., and Gong, X. J. S. N., (2023). "Assessment of tomographic window and sampling rate effects on GNSS water vapor tomography". v. 4, no. 1, p. 7. [ DOI:10.1186/s43020-023-00096-4] 7. Sharifi, M.A., Souri, A.H., (2014). "A hybrid LS-HE and LS-SVM model to predict time series of precipitable water vapor derived from GPS measurements". Arabian Journal of Geosciences, 8(8), 7257-7272. [ DOI:10.1007/s12517-014-1716-0] 8. Zheng, D., Hu, Y., Wang, W.S.J., Zhu, M. C., (2015). "Research on regional zenith tropospheric delay based on neural network technology". Survey Review 47(343):286-295. [ DOI:10.1179/1752270614Y.0000000130] 9. Suparta, W., Alhasa, K. M., (2016). "Modeling of Tropospheric Delays Using ANFIS". Earth and Environmental Science,
https://doi.org/10.1007/978-3-319-28437-8 [ DOI:10.1007/978-3-319-28437-8.] 10. Sam Khaniani, A., Motieyan, H., Mohammadi, A., (2021). "Rainfall forecast based on GPS PWV together with meteorological parameters using neural network models". Journal of Atmospheric and Solar-Terrestrial Physics, 214(105533). [ DOI:10.1016/j.jastp.2020.105533] 11. Ghaffari Razin, M.R., Inyurt, S., (2022). "Spatiotemporal analysis of precipitable water vapor using ANFIS and comparison against voxel‑based tomography and radiosonde". GPS Solutions (2022) 26:1,
https://doi.org/10.1007/s10291-021-01184-1 [ DOI:10.1007/s10291-021-01184-1.] 12. Davis, J.L., Herring, T.A., Shapiro, II., Rogers, E.E., Elgered, G., (1985). "Geodesy by radio interferometry: effects of atmospheric modeling errors on estimates of baseline length". Radio Sci 20(6):1593-1607. [ DOI:10.1029/RS020i006p01593] 13. Bevis, M., Businger, S., Herring, T., Rocken, C., Ware, R.H., (1992). "GPS metrology: remote sensing of atmospheric water vapor using the global positioning system". J Geophys Res 97(D14): 15787-15801. [ DOI:10.1029/92JD01517] 14. Cortes, C., Vapnik, V., (1995). "Support-vector networks. Machine Learning". 20(3), 273-297. [ DOI:10.1007/BF00994018] 15. Smola, A. J., Schölkopf, B., (1998). "On a kernel-based method for pattern recognition, regression, approximation, and operator inversion". Algorithmica, 22(1), 211-231. [ DOI:10.1007/PL00013831] 16. غفاری رزین، س.ر.، (1399). "ارزیابی کارایی سامانه استنتاج عصبی-فازی سازگار در مدلسازی بخار آب مایل وردسپهر". مجله فیزیک زمین و فضا، دوره 47، شماره 2، مرداد 1400، صفحه 257-272. 17. Yao, Y., Xin, L., Zhao, O., (2019). "An improved pixel-based water vapor tomography model". Ann. Geophys (37):89-100, doi.org/10.5194/angeo-37-89-2019. [ DOI:10.5194/angeo-37-89-2019] 18. Aster, R., Borchers, B., Thurber, C., (2003). "Parameter estimation and inverse problems". vol 90. Elsevier Academic Press, USA. 19. Matheron, G., (1971). "The theory of regionalized variables, and its applications". Centre de Geostatistique, Fontainebleau, Paris, 1971. 20. Joseph, V. R., (2006). "Limit Kriging". Technometrics, 48(4):458-466. [ DOI:10.1198/004017006000000011] 21. Erdogan, S., (2010). "Modeling the spatial distribution of DEM error with geographically weighted regression: An experimental study". Comput. Geosci. 36(1): 34-43. [ DOI:10.1016/j.cageo.2009.06.005] 22. Li, J., Heap, A. D., (2008). "A Review of Spatial Interpolation Methods for Environmental Scientists". Geoscience Australia Canberra, 137p. 23. Landskron, D., Böhm, J., (2017). "VMF3/GPT3: refined discrete and empirical troposphere mapping functions". Journal of Geodesy, 92(4), 349-360. [ DOI:10.1007/s00190-017-1066-2] 24. Askne, J., Nordius, H., (1987). "Estimation of tropospheric delay for microwaves from surface weather data". 22(3): 379-386. [ DOI:10.1029/RS022i003p00379] 25. Dousa, J., Bennitt, G. V., (2013). "Estimation and Evaluation of Hourly Updated Global Zenith Total Delays over Ten Months", GPS Solut.17, 453-464. [ DOI:10.1007/s10291-012-0291-7] |
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Cheginin M, Voosoghi B, Ghaffari-Razin S R. Estimation of precipitable water vapor using least squares support vector regression and comparison with other models. JGST 2024; 13 (3) : 2 URL: http://jgst.issgeac.ir/article-1-1154-en.html
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