Appraisal of ANN and ANFIS for Predicting Vertical Total Electron Content (VTEC) in the Ionosphere for GPS Observations
Keywords:
Artificial Neural Network, Adaptive Neuro Fuzzy Inference System, Vertical Total Electron ContentAbstract
Positional accuracy in the usage of GPS receiver is one of the major challenges in GPS observations. The propagation of the GPS signals through the ionosphere experience speed reduction cause by refraction of the radio waves. These signals are interfered by free electrons which are the massive particles in the ionosphere region. This results in delays in the transition of signals as it passes through the ionosphere to the Earth. Therefore, the total electron content is a key parameter in mitigating ionospheric effects on GPS receivers of the affected signals. Many researchers have therefore proposed various models and methods for predicting the electron content along the signal path that is responsible for the deviation in the GPS signals. This paper will focus on the use of two different models for predicting the Vertical Total Electron Content (VTEC). In view of that, Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) algorithms have been developed for the prediction of VTEC in the ionosphere. The developed ANN and ANFIS model gave Root Mean Square Error (RMSE) of 1.953 and 1.190 respectively. From the results obtained it can be stated that the ANFIS is more suitable tool for the prediction of VTEC.
References
Acharya, R., Roy, B., Sivaraman, M. R. and Dasgupta, A. (2011) “Prediction of Ionospheric Total Electron Content using Adaptive Neural Network with in-situ Learning Algorithmâ€, Advances in Space Research, Vol. 47, No. 1, pp. 115-123.
Akyilmaz, O. and Kutterer, H. (2004), “Prediction of Earth Rotation Parameters by Fuzzy Inference Systemsâ€, Journal of Geodesy, No. 78, pp. 82-92.
Anon., (2017), “China Cities and Provincesâ€, www.chinatoday.com/city/jiangsu.htm. Accessed: March 15, 2017.
Codrescu, M. (2007), “The influence of the ionosphere on GPS Operationsâ€, Boulder, Colorado, 31 pp.
Fan, G., Wang, W. and Xi, X. (2010), “Modeling of Ionosphere VTEC using Generalized Regression Neural Network†Acta Geodaetica et Cartographica Sinica, Vol. 39, No. 1, pp. 16-21.
Friedrich, M., Fankhauser, M., Oyeyemi, E. and McKinnell, L.A. (2008), “A Neural Network-Based Ionospheric Model for Areciboâ€, Advances in Space Research, Vol. 42, No. 4, pp. 776-781.
Habarulema, J. B., McKinnell, L. A. and Cilliers, P. J. (2007), “Prediction of Global Positioning System Total Electron Content using Neural Networks Over South Africa†Journal of Atmospheric and Solar-Terrestrial Physics, Vol. 69, No. 15, pp. 1842-1850.
Hernandezâ€Pajares, M., Juan, J. M. and Sanz, J., (1997), “Neural Network Modeling of the Ionospheric Electron Content at Global Scale using GPS Dataâ€, Radio Science, Vol. 32, No. 3, pp. 1081-1089.
Homam, M. J. (2016), “Prediction of Total Electron Content of the Ionosphere Using Neural Networkâ€, Jurnal Teknologi, Vol. 78, pp. 53-57.
Hu, W. S., Zheng, D. Y. and Nie, W. F (2014), “Research on Methods of Regional Ionospheric Delay Correction based on Neural Network Technologyâ€, Survey Review, Vol. 46, pp. 1-8.
Jang, J. R. (1993), “Adaptive Network Based Fuzzy Inference Systemâ€, IEEE Transactions on System Man and Cybernetics, Vol. 23, No. 3, pp. 665-685.
Kersley, L., Malan, D., Pryse, S. E., Cander, L. R., Bamford, R. A., Belehaki, A., Leitinger, R., Radicella, S. M., Mitchell, C. N. and Spencer, P. S. J. (2004), “Total Electron Content – A Key Parameter in Propagation: Measurement and use in Ionospheric Imagingâ€, Vol. 47, No. 2/3, pp. 1068-1091.
Leandro, R. F. and Santos, M. C. (2007), “A Neural Network Approach for Regional Vertical Total Electron Content Modellingâ€, In Stud. Geophys. Geod., Vol. 51, pp. 279-292.
Leandro, R. and Santos, M., (2004), “Regional Computation of TEC using a Neural Network Model†AGU, CGU, SEG and EEGS Joint Assembly, Montreal, Canada, 1 pp.
Liu, L. Huang, L., Chen, J., Wu, P. and Qin, X. (2017), “Applicability Analysis of VTEC Derived from Sophisticated Klobuchar Model in Chinaâ€, ISPRS International Journal of Geo-Information, Vol. 6, No. 75, pp. 1-15.
Ma, X. F., Maruyama, T., Ma, G. and Takeda, T. (2005), “Threeâ€Dimensional Ionospheric Tomography using Observation data of GPS Ground Receivers and Ionosonde by Neural Networkâ€, Journal of Geophysical Research: Space Physics, Vol.110, pp. 2156-2202.
Markovic, M. (2014), “Determination of Total Electron Content in the Ionosphere using GPS Technologyâ€, Geonauka, Vol. 2, No. 2, pp. 1-9.
Mayilvaganan, M. K. and Naidu, K. B. (2011), “Comparative Study of ANN and ANFIS for the Prediction of Groundwater Level of a Watershedâ€, Global Journal of Mathematical Science: Theory and Practical, Vol. 3, No. 4, pp. 299-306.
Ratnam, D. V., Dinesh, B. V., Tejaswi, B., Kumar, D. P., Ritesh, T. V., Brahmanadam, P. S., and Vindhya, G. (2012), “TEC Prediction Model using Neural Networks over a Low Latitude GPS Stationâ€, International Journal of Soft Computing and Engineering (IJSCE), Vol. 2, Issue 2, pp. 517-521.
Tetley, L. and Calcut, D. (2001), “Electronic Navigation Systemâ€, 3rd edition, Butterworth-Heinemann, Britain, 436 pp.
Tulunay, E., Senalp, E. T., Radicella, S. M., and Tulunay, Y. (2006), “Forecasting Total Electron Content Maps by Neural Network Techniqueâ€, Radio Sci., Vol. 41, No. 4, pp. 4- 16.
Wang, J. S. (2003), “An efficient recurrent neuro-fuzzy system for identification and control of dynamic systemsâ€. In Proceedings of the IEEE International Conference on Systems, Manand Cybernetics, Washington, DC, USA, 5th to 8th October, 2003.
Wasserman P. D., (1993) “Advanced Methods in Neural Computing,†Van Nostrand Reinhold, New York, 1993, 255 pp
Yang, I.H.; Yeo, M.S.; Kim, K.W. (2003), “Application of artificial neural network to predict the optimal start time for heating system in buildingâ€. Energy Convers. Manag. No. 44, pp. 2791–2809.
Downloads
Published
Issue
Section
License
Copyright © 2021 University of Mines and Technology (UMaT), Tarkwa. Ghana
