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Application of Wavelet Neural Networks for Improving of Ionospheric Tomography Reconstruction over Iran | ||
| فیزیک زمین و فضا | ||
| مقاله 9، دوره 44، شماره 4، دی 1397، صفحه 99-114 اصل مقاله (1.11 M) | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2018.245567.1006940 | ||
| نویسندگان | ||
| Mir Reza Ghaffari Razin* 1؛ Behzad Voosoghi2 | ||
| 1Assistant Professor, Department of Surveying Engineering, Arak University of Technology, Arak, Iran | ||
| 2Associate Professor, Department of Geodesy, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi Univ. of Technology, Tehran, Iran | ||
| چکیده | ||
| In this paper, a new method of ionospheric tomography is developed and evaluated based on the neural networks (NN). This new method is named ITNN. In this method, wavelet neural network (WNN) with particle swarm optimization (PSO) training algorithm is used to solve some of the ionospheric tomography problems. The results of ITNN method are compared with the residual minimization training neural network (RMTNN) and modified RMTNN (MRMTNN). In all three methods, empirical orthogonal functions (EOFs) are used as a vertical objective function. To apply the methods for constructing a 3D-image of the electron density, GPS measurements of the Iranian permanent GPS network (in three days in 2007) are used. Besides, two GPS stations from international GNSS service (IGS) are used as test stations. The ionosonde data in Tehran (φ=35.73820, λ=51.38510) has been used for validating the reliability of the proposed methods. The minimum RMSE for RMTNN, MRMTNN, ITNN are 0.5312, 0.4743, 0.3465 (1011ele./m3) and the minimum bias are 0.4682, 0.3890, and 0.3368 (1011ele./m3) respectively. The results indicate the superiority of ITNN method over the other two methods. | ||
| کلیدواژه‌ها | ||
| Tomography؛ RMTNN؛ MRMTNN؛ ITNN؛ GPS | ||
| عنوان مقاله [English] | ||
| Application of Wavelet Neural Networks for Improving of Ionospheric Tomography Reconstruction over Iran | ||
| نویسندگان [English] | ||
| Mir Reza Ghaffari Razin1؛ Behzad Voosoghi2 | ||
| 1Assistant Professor, Department of Surveying Engineering, Arak University of Technology, Arak, Iran | ||
| 2Associate Professor, Department of Geodesy, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi Univ. of Technology, Tehran, Iran | ||
| چکیده [English] | ||
| In this paper, a new method of ionospheric tomography is developed and evaluated based on the neural networks (NN). This new method is named ITNN. In this method, wavelet neural network (WNN) with particle swarm optimization (PSO) training algorithm is used to solve some of the ionospheric tomography problems. The results of ITNN method are compared with the residual minimization training neural network (RMTNN) and modified RMTNN (MRMTNN). In all three methods, empirical orthogonal functions (EOFs) are used as a vertical objective function. To apply the methods for constructing a 3D-image of the electron density, GPS measurements of the Iranian permanent GPS network (in three days in 2007) are used. Besides, two GPS stations from international GNSS service (IGS) are used as test stations. The ionosonde data in Tehran (φ=35.73820, λ=51.38510) has been used for validating the reliability of the proposed methods. The minimum RMSE for RMTNN, MRMTNN, ITNN are 0.5312, 0.4743, 0.3465 (1011ele./m3) and the minimum bias are 0.4682, 0.3890, and 0.3368 (1011ele./m3) respectively. The results indicate the superiority of ITNN method over the other two methods. | ||
| کلیدواژه‌ها [English] | ||
| Tomography, RMTNN, MRMTNN, ITNN, GPS | ||
| مراجع | ||
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Amerian, Y., Mashhadi Hossainali, M., Voosoghi, B. and Ghaffari razin, M. R, 2010, Tomographic reconstruction of the ionospheric electron density in term of wavelets. Journal of Aerospace Science and Technology, 7(1), 19–29. Austen, J. R., Franke, S. J. and Liu, C. H., 1988, Ionospheric imaging using computerized tomography. Radio Sci., 23, 299–307. Alexandridis, A. and Zapranis, A., 2013, Wavelet neural networks: A practical guide. Neural Networks, 42 1–27. Ghaffari Razin, M. R., 2015, Development and analysis of 3D ionosphere modeling using base functions and GPS data over Iran. Acta Geod. Geophys., DOI 10.1007/s40328-015-0113-9, 51(1) , 95-111. Ghaffari Razin, M. R. and Voosoghi, B., 2016, Regional ionosphere modeling using spherical cap harmonics and empirical orthogonal functions over Iran. Acta Geod. Geophys., 52(1), 19-33, doi: 10.1007/s40328-016-0162-8. Ghaffari Razin, M. R., Voosoghi, B. and Mohammadzadeh, A., 2015, Efficiency of artificial neural networks in map of total electron content over Iran. Acta Geod. Geophys., DOI 10.1007/s40328-015-0143-3. Ghaffari Razin, M. R. and Voosoghi, B., 2016, Regional application of multi-layer artificial neural networks in 3-D ionosphere tomography. Advances in Space Research. http://dx.doi.org/10.1016/j.asr.2016.04.029. Hirooka, S., Hattori, K. and Takeda, T., 2011, Numerical validations of neural-network-based ionospheric tomography for disturbed ionospheric conditions and sparse data. Radio Sci., 46(5), RS0F05, DOI: 10.1029/2011RS004760. Habarulema, J. B., McKinnell, L.-A. and Opperman, B. D. L., 2009, A recurrent neural network approach Kunitsyn, V. E., Nesterov, I. A., Padokhin, A. M. and Tumanova, U. S., 2011, Ionospheric Radio Tomography based on the GPS/GLONASS navigation systems. J. Commun. Technol. Electron., 56(11), 1269-1281, doi: 10.1134/S1064226911100147. Kennedy, J. and Eberhart, R., 1995, Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, 4(ICNN ’95), 1942–1948, Perth, Western Australia, November-December 1995. Liaqat, A., Fukuhara, M. and Takeda, T., 2003, optimal estimation of parameter of dynamical system by neural network collocation method. Comput. Phys. Commun., 150, 215–234, doi:10.1016/S0010-4655(02) 00680-X, 2003. Ma, X. F., Maruyama, T., Ma, G. and Takeda, T., 2005, Three dimensional ionospheric tomography using observation data of GPS ground receivers and ionosonde by neural network. J. Geophys. Res., 110, A05308, doi: 10.1029/2004JA010797. Pokhotelov, D., Jayachandran, P., Mitchell, C. N., MacDougall, J. W. and Denton, Quarteroni, A., Sacco, R. and Saleri, F., 2007, Numerical Mathematics, 37, Texts in Applied Mathematics, 2nd ed., Springer Berlin Heidelberg, Heidelberg, Germany. Van de Kamp, M. M. J. L., 2013, Medium-scale 4-D ionospheric tomography using a dense GPS network. Ann Geophys., doi:10.5194/angeo-31-75-2013. Wen, D. B., Wang, Y. and Norman, R., 2012, A new two-step algorithm for ionospheric tomography solution. GPS Solut., 16(1), 89–94, Jan./Feb. Yilmaz, A., Akdogan, K. E. and Gurun, M., 2009, Regional TEC mapping using neural networks. Radio Sci., 44, RS3007, doi:10.1029/2008RS004049. Yizengaw, E., Moldwin, M. B., Dyson, P. L. and Essex, E. A., 2007, Using Tomography of GPS TEC to Routinely Determine Ionospheric Average Electron Density Profiles. Journal of Atmospheric and Solar-Terrestrial Physics, 69, 314-321. Zhang, Q. and Benveniste, A., 1992, Wavelet Networks. IEEE Trans. Neural Networks, 3(6), 889–898. | ||
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