Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review

Nor’asnilawati Salleh, Siti Sophiayati Yuhaniz, Sharizal Fadlie Sabri, Nurulhuda Firdaus Mohd Azmi


This paper studies the machine learning techniques that can be used to enhance the prediction method of the ionosphere for space weather monitoring. Previously, the empirical model is used. However, there is a large deviation of the total electron content of ionosphere data for the areas located in the equatorial and low-latitude regions due to the lack of observation data contributed by these areas during the development of the empirical model. The machine learning technique is an alternative method used to develop the predictive model. Thus, in this study, the machine learning techniques that can be applied are investigated. The aim is to improve the predictive model in terms of reducing the total electron content deviation, increasing the accuracy and minimizing the error. In this review, the techniques used in previous works will be compared. The artificial neural network is found to be a suitable technique and the most favorable from the review conducted. Also, this technique can provide an accurate model for time series data and fewer errors compared to other techniques. However, due to the size and complexity of the data, the deep neural network technique that is an improved artificial neural network technique is suggested. By using this technique, an accurate ionosphere predictive model in equatorial and low region area is expected. In the future, this study will analyze further by using computing tools and real-time data.


prediction; ionosphere; space weather; machine learning; data analytic.

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M. Lockwood, M. J. Owens, L. A. Barnard, C. J. Scott, C. E. Watt, and S. Bentley, “Space climate and space weather over the past 400 years: 2. Proxy indicators of geomagnetic storm and substorm occurrence,” J. Sp. Weather Sp. Clim., vol. 8, no. 2004, p. A12, 2018.

L. A. Hayes, P. T. Gallagher, J. McCauley, B. R. Dennis, J. Ireland, and A. Inglis, “Pulsations in the Earth’s Lower Ionosphere Synchronized with Solar Flare Emission,” J. Geophys. Res. Sp. Phys., vol. 122, no. 10, pp. 9841–9847, 2017.

R. G. Ezquer, L. A. Scidá, Y. M. Orué, B. Nava, M. A. Cabrera, and C. Brunini, “NeQuick 2 and IRI Plas VTEC predictions for low latitude and South American sector,” Adv. Sp. Res., vol. 61, no. 7, pp. 1803–1818, 2018.

F. Sabzehee, S. Farzaneh, M. A. Sharifi, and M. Akhoondzadeh, “TEC Regional Modeling and prediction using ANN method and single frequency receiver over IRAN,” Ann. Geophys., vol. 61, no. 1, p. 103, 2018.

J. Kleissl, Solar Energy Forecasting and Resource Assessment. Elsevier Science, 2013.

J. Strickland, Predictive Analytics using R. Lulu.com, 2015.

S. Mohanty, P. K. Patra, and S. S. Sahoo, “Prediction and application of solar radiation with soft computing over traditional and conventional approach--a comprehensive review,” Renew. Sustain. Energy Rev., vol. 56, pp. 778–796, 2016.

C. Voyant et al., “Machine learning methods for solar radiation forecasting: A review,” Renew. Energy, vol. 105, pp. 569–582, 2017.

F. Almonacid, E. F. Fernandez, A. Mellit, and S. Kalogirou, “Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology,” Renew. Sustain. Energy Rev., vol. 75, pp. 938–953, 2017.

R. Chandra and S. Chand, “Evaluation of co-evolutionary neural network architectures for time series prediction with mobile application in finance,” Appl. Soft Comput., vol. 49, pp. 462–473, 2016.

M. Čelan and M. Lep, “Bus arrival time prediction based on network model,” Procedia Comput. Sci., vol. 113, pp. 138–145, 2017.

E. Disse et al., “An artificial neural network to predict resting energy expenditure in obesity,” Clin. Nutr., vol. 37, no. 5, pp. 1661–1669, 2018.

J. Wang et al., “Statistical analysis and verification of 3-hourly geomagnetic activity probability predictions,” Sp. Weather, vol. 13, no. 12, pp. 831–852, 2015.

G. Najafi et al., “SVM and ANFIS for prediction of performance and exhaust emissions of a SI engine with gasoline–ethanol blended fuels,” Appl. Therm. Eng., vol. 95, pp. 186–203, 2016.

H. Z. Sabzi, J. P. King, and S. Abudu, “Developing an intelligent expert system for streamflow prediction, integrated in a dynamic decision support system for managing multiple reservoirs: A case study,” Expert Syst. Appl., vol. 83, pp. 145–163, 2017.

A. M. Bagirov, A. Mahmood, and A. Barton, “Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach,” Atmos. Res., vol. 188, pp. 20–29, 2017.

X. Chen, X. Chen, J. She, and M. Wu, “A hybrid time series prediction model based on recurrent neural network and double joint linear–nonlinear extreme learning network for prediction of carbon efficiency in iron ore sintering process,” Neurocomputing, vol. 249, pp. 128–139, 2017.

C. Liu, C. Liu, Y. Shang, S. Chen, B. Cheng, and J. Chen, “An adaptive prediction approach based on workload pattern discrimination in the cloud,” J. Netw. Comput. Appl., vol. 80, pp. 35–44, 2017.

A. Stepchenko, J. Chizhov, L. Aleksejeva, and J. Tolujew, “Nonlinear, non-stationary and seasonal time series forecasting using different methods coupled with data preprocessing,” Procedia Comput. Sci., vol. 104, pp. 578–585, 2017.

M. Tshisaphungo, J. B. Habarulema, and L.-A. McKinnell, “Modeling ionospheric foF2 response during geomagnetic storms using neural network and linear regression techniques,” Adv. Sp. Res., vol. 61, no. 12, pp. 2891–2903, 2018.

X. Zhao, B. Ning, L. Liu, and G. Song, “A prediction model of short-term ionospheric foF2 based on AdaBoost,” Adv. Sp. Res., vol. 53, no. 3, pp. 387–394, 2014.

A. Zhukov, D. Sidorov, A. Mylnikova, and Y. Yasyukevich, “Machine learning methodology for ionosphere total electron content nowcasting,” Int. J. Artif. Intell., vol. 16, no. 1, pp. 144–157, 2018.

J. Xin, J. Zhou, S. Yang, X. Li, and Y. Wang, “Bridge structure deformation prediction based on GNSS data using Kalman-ARIMA-GARCH model,” Sensors, vol. 18, no. 1, p. 298, 2018.

J. A. Lazzús, P. Vega, P. Rojas, and I. Salfate, “Forecasting the Dst index using a swarm-optimized neural network,” Sp. Weather, vol. 15, no. 8, pp. 1068–1089, 2017.

G. Sivavaraprasad and D. V. Ratnam, “Performance evaluation of ionospheric time delay forecasting models using GPS observations at a low-latitude station,” Adv. Sp. Res., vol. 60, no. 2, pp. 475–490, 2017.

M. Nekkaa and D. Boughaci, “A memetic algorithm with support vector machine for feature selection and classification,” Memetic Comput., vol. 7, no. 1, pp. 59–73, 2015.

S. S. Abadeh, P. M. M. Esfahani, and D. Kuhn, “Distributionally robust logistic regression,” in Advances in Neural Information Processing Systems, 2015, pp. 1576–1584.

Y. Kong, H. Chai, J. Li, Z. Pan, and Y. Chong, “A modified forecast method of ionosphere VTEC series based on ARMA model,” in 2017 Forum on Cooperative Positioning and Service (CPGPS), 2017, pp. 90–95.

A. Qazi, H. Fayaz, A. Wadi, R. G. Raj, N. A. Rahim, and W. A. Khan, “The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review,” J. Clean. Prod., vol. 104, pp. 1–12, 2015.

A. Tebabal, S. M. Radicella, M. Nigussie, B. Damtie, B. Nava, and E. Yizengaw, “Local TEC modelling and forecasting using neural networks,” J. Atmos. Solar-Terrestrial Phys., vol. 172, pp. 143–151, 2018.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, p. 436, 2015.

J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural networks, vol. 61, pp. 85–117, 2015.

W. Sun, L. Xu, X. Huang, W. Zhang, T. Yuan, and Y. Yan, “Bidirectional LSTM for ionospheric vertical Total Electron Content (TEC) forecasting,” in 2017 IEEE Visual Communications and Image Processing (VCIP), 2017, pp. 1–4.

A. Alzahrani, P. Shamsi, C. Dagli, and M. Ferdowsi, “Solar irradiance forecasting using deep neural networks,” Procedia Comput. Sci., vol. 114, pp. 304–313, 2017.

J. B. Heaton, N. G. Polson, and J. H. Witte, “Deep learning in finance,” arXiv Prepr. arXiv1602.06561, 2016.

Y. Amerian, M. M. Hossainali, and B. Voosoghi, “Regional improvement of IRI extracted ionospheric electron density by compactly supported base functions using GPS observations,” J. Atmos. Solar-Terrestrial Phys., vol. 92, pp. 23–30, 2013.

S.-S. Jan and A.-L. Tao, “Comprehensive comparisons of satellite data, signals, and measurements between the BeiDou navigation satellite system and the global positioning system,” Sensors, vol. 16, no. 5, p. 689, 2016.

A. A. Ferreira, R. A. Borges, C. Paparini, and S. M. Radicella, “TEC modelling via neural network using observations from the first GLONASS R&D data network in Brazil and the RBMC,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 12829–12834, 2017.

A. A. Ferreira, R. A. Borges, C. Paparini, L. Ciraolo, and S. M. Radicella, “Short-term estimation of GNSS TEC using a neural network model in Brazil,” Adv. Sp. Res., vol. 60, no. 8, pp. 1765–1776, 2017.

DOI: http://dx.doi.org/10.18517/ijaseit.10.1.10163


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