1. T. Nilsson, J. Böhm, D. D. Wijaya, A. Tresch, V. Nafisi, and H. Schuh, "Path delays in the neutral atmosphere," Atmospheric effects in space geodesy, pp. 73-136: Springer, 2013. [ DOI:10.1007/978-3-642-36932-2_3] 2. E. K. Smith, and S. Weintraub, "The constants in the equation for atmospheric refractive index at radio frequencies," Proceedings of the IRE, vol. 41, no. 8, pp. 1035-1037, 1953. [ DOI:10.1109/JRPROC.1953.274297] 3. B. Chen, W. Dai, Z. Liu, L. Wu, and P. Xia, "Assessments of GMI-derived precipitable water vapor products over the south and east China seas using radiosonde and GNSS," Advances in Meteorology, vol. 2018, 2018. [ DOI:10.1155/2018/7161328] 4. P. J. Davis, Interpolation and approximation: Courier Corporation, 1975. 5. D.-S. Kim, J.-H. Won, H.-I. Kim, K.-H. Kim, and K.-D. Park, "Accuracy analysis of GPS-derived precipitable water vapor according to interpolation methods of meteorological data," Spatial Information Research, vol. 18, no. 4, pp. 33-41, 2010. 6. C. Liu, N. Zheng, K. Zhang, and J. Liu, "A new method for refining the GNSS-derived precipitable water vapor map," Sensors, vol. 19, no. 3, pp. 698, 2019. [ DOI:10.3390/s19030698] 7. S. R. Ghaffari-Razin and N. Hooshangi, "Estimation of Precipitable Water Vapor (PWV) using learning-based methods in north-west of Iran", Geographic Information, vol. 30, no. 120, pp. 139-155, 2022. 8. X. Ma, Y. Yao, B. Zhang, M. Yang, and H. Liu, "Improving the accuracy and spatial resolution of precipitable water vapor dataset using a neural network-based downscaling method," Atmospheric Environment, vol. 269, pp. 118850, 2022. [ DOI:10.1016/j.atmosenv.2021.118850] 9. V. Mendes, "Modeling the neutral-atmospheric propagation delay in radiometric space techniques," UNB geodesy and geomatics engineering technical report, no. 199, 1999. 10. J. Saastamoinen, "Atmospheric correction for the troposphere and stratosphere in radio ranging satellites," The use of artificial satellites for geodesy, vol. 15, pp. 247-251, 1972. [ DOI:10.1029/GM015p0247] 11. M. Bevis, S. Businger, T. A. Herring, C. Rocken, R. A. Anthes, and R. H. Ware, "GPS meteorology: Remote sensing of atmospheric water vapor using the global positioning system," Journal of Geophysical Research: Atmospheres, vol. 97, no. D14, pp. 15787-15801, 1992. [ DOI:10.1029/92JD01517] 12. C. J. Trahan, and R. E. Wyatt, "Radial basis function interpolation in the quantum trajectory method: optimization of the multi-quadric shape parameter," Journal of Computational Physics, vol. 185, no. 1, pp. 27-49, 2003. [ DOI:10.1016/S0021-9991(02)00046-3] 13. M. A. Sharifi, "Comparison of the geodetic height correcting surface determination methods: A case study for Tehran," Iranian Journal of Geophysics, vol. 10, no. 3, pp. 40-52, 2016. 14. R. Franke, "Scattered data interpolation: tests of some methods," Mathematics of computation, vol. 38, no. 157, pp. 181-200, 1982. [ DOI:10.1090/S0025-5718-1982-0637296-4] 15. W. Keller, and A. Borkowski, "Thin plate spline interpolation," Journal of Geodesy, vol. 93, no. 9, pp. 1251-1269, 2019. [ DOI:10.1007/s00190-019-01240-2] 16. Z. Tang, K. Chen, M. Pan, M. Wang, and Z. Song, "An augmentation strategy for medical image processing based on statistical shape model and 3D thin plate spline for deep learning," IEEE Access, vol. 7, pp. 133111-133121, 2019. [ DOI:10.1109/ACCESS.2019.2941154] 17. V. Balek, and I. Mizera, "Mechanical models in nonparametric regression," From Probability to Statistics and Back: High-Dimensional Models and Processes-A Festschrift in Honor of Jon A. Wellner, pp. 5-19, 2013. [ DOI:10.1214/12-IMSCOLL902] 18. G. Zhang, B. E. Patuwo, and M. Y. Hu, "Forecasting with artificial neural networks:: The state of the art," International journal of forecasting, vol. 14, no. 1, pp. 35-62, 1998. [ DOI:10.1016/S0169-2070(97)00044-7] 19. Y. Kocyigit, A. Alkan, and H. Erol, "Classification of EEG recordings by using fast independent component analysis and artificial neural network," Journal of Medical Systems, vol. 32, no. 1, pp. 17-20, 2008. [ DOI:10.1007/s10916-007-9102-z] 20. A. Subasi, "Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients," Expert systems with applications, vol. 28, no. 4, pp. 701-711, 2005. [ DOI:10.1016/j.eswa.2004.12.027] 21. S. N. Oğulata, C. Şahin, and R. Erol, "Neural network-based computer-aided diagnosis in classification of primary generalized epilepsy by EEG signals," Journal of medical systems, vol. 33, no. 2, pp. 107-112, 2009. [ DOI:10.1007/s10916-008-9170-8] 22. U. Orhan, M. Hekim, and M. Ozer, "EEG signals classification using the K-means clustering and a multilayer perceptron neural network model," Expert Systems with Applications, vol. 38, no. 10, pp. 13475-13481, 2011. [ DOI:10.1016/j.eswa.2011.04.149] 23. R. Zhang, Y. Shen, Z. Tang, W. Li, and D. Zhang, "A Review of Numerical Research on the Pressure Swing Adsorption Process," Processes, vol. 10, no. 5, pp. 812, 2022. [ DOI:10.3390/pr10050812] 24. I. A. Basheer, and M. Hajmeer, "Artificial neural networks: fundamentals, computing, design, and application," Journal of microbiological methods, vol. 43, no. 1, pp. 3-31, 2000. [ DOI:10.1016/S0167-7012(00)00201-3] 25. D. Shepard, "A two-dimensional interpolation function for irregularly-spaced data." pp. 517-524. 26. D. D. Sarma, Geostatistics with applications in earth sciences: Springer Science & Business Media, 2010. [ DOI:10.1007/978-1-4020-9380-7] 27. I. Sayin, F. Arikan, and O. Arikan, "Regional TEC mapping with random field priors and kriging," Radio Science, vol. 43, no. 05, pp. 1-14, 2008. [ DOI:10.1029/2007RS003786] 28. A. Lichtenstern, "Kriging methods in spatial statistics," 2013.
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