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Performance analysis of Neural Networks with IRI-2016 and IRI-2012 models over Indian low-latitude GPS stations

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Abstract

The Global Positioning System (GPS) applications are highly vulnerable to the ionospheric space weather effects. Modelling and forecasting the ionospheric effects such as time delays for GPS signals are important for real time alerts of space weather effects on GPS services. In the present work, the performance of Neural Networks (NN) model is compared with International Reference Ionosphere (IRI) models. The ionospheric Total Electron Content (TEC) observations have been collected during 2015 year, descending phase of 24th solar cycle, over three Indian low latitude GPS stations namely, Bengaluru (Geographic coordinates: 13.02°N and 77.57°E), near to geomagnetic equator, Guntur (Geographic coordinates: 16.37°N and 80.37°E), which is at Equatorial Ionization Anomaly (EIA) and Lucknow (Geographic coordinates: 26.83°N and 80.92°E), which is beyond EIA region. The performance of NN model in predicting the ionospheric TEC values is compared with IRI (IRI-2012 and IRI-2016) models during test period, October–December 2015 over three Indian low latitude regions. It is observed that IRI models (IRI-2012 and IRI-2016) have shown more the temporal differences with GPS-VTEC during sunrise hours compared to sunset hours over three low latitude regions. The performance of IRI-2016 model has apparently better than IRI-2012 model. However, it is observed that IRI-2016 model has large discrepancies over Bengaluru and Guntur station due to high VTEC fluctuations at equatorial and low latitudes. The NN models are well predicted the measured diurnal mean VTEC variations with the less errors, ±5 TECU but the differences of IRI models are ±15 TECU over all the three stations. Later, GPS-data for 10 years, 2009–2018, is collected over Bengaluru station during 24th solar cycle. The performance of NN model is validated during 2016, 2017 and 2018 years over Bengaluru GPS station. The error measurements and experimental results reported that the measured GPS-VTEC values are well predicted by NN model compared to IRI-2016 model over equatorial and low latitude GPS stations.

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Acknowledgements

The present work has been carried out under the research projects sponsored by Science and Engineering Research Board vide sanction order No: ECR/2018/001701 with title (Implementation of Deep Learning Algorithms to Develop Web based Ionospheric Time Delays Forecasting System over Indian Region using Ground based GNSS and NAVigation with Indian Constellation (NAVIC)observations), File No. ECR/2015/000410 with title (“Development of Ionospheric TEC Data Assimilation Model based on Kalman Filter using ground and space-based GNSS and Ionosonde Observations”) and Department of Science and Technology, New Delhi, India for funding this research through SR/FST/ET-II/2019/450 FIST program.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India

    Lakshmi Mallika I, D. Venkata Ratnam & G. Sivavaraprasad

  2. Faculty, California State University, Long Beach and Fullerton Campus, CA, USA

    Saravana Raman

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  1. Lakshmi Mallika I
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  2. D. Venkata Ratnam
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  3. Saravana Raman
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  4. G. Sivavaraprasad
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Correspondence to D. Venkata Ratnam.

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Mallika I, L., Ratnam, D.V., Raman, S. et al. Performance analysis of Neural Networks with IRI-2016 and IRI-2012 models over Indian low-latitude GPS stations. Astrophys Space Sci 365, 124 (2020). https://doi.org/10.1007/s10509-020-03821-6

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