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The Economic Impact of Space Weather: Where Do We Stand?

Risk analysis, 2017-02, Vol.37 (2), p.206-218

2017

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Estimating the geoeffectiveness of halo CMEs from associated solar and IP parameters using neural networks

Annales geophysicae (1988), 2012-06, Vol.30 (6), p.963-972

2012

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Investigating the performance of neural network backpropagation algorithms for TEC estimations using South African GPS data

Annales geophysicae (1988), 2012-05, Vol.30 (5), p.857-866

2012

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Topside ionospheric vertical electron density profile reconstruction using GPS and ionosonde data: possibilities for South Africa

Annales geophysicae (1988), 2011-02, Vol.29 (2), p.229-236

2011

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Present day challenges in understanding the geomagnetic hazard to national power grids

Advances in space research, 2010-05, Vol.45 (9), p.1182-1190

2010

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Solar and interplanetary precursors of geomagnetic storms in solar cycle 23

Advances in space research, 2013-02, Vol.51 (3), p.395-410

2013

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Statistical investigation of horizontal propagation of gravity waves in the ionosphere over Europe and South Africa

Journal of Geophysical Research: Space Physics, 2012-03, Vol.117 (A3), p.n/a

2012

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Observations of wave activity in the ionosphere over South Africa in geomagnetically quiet and disturbed periods

Advances in space research, 2012-07, Vol.50 (2), p.182-195

2012

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Progress towards a new global foF2 model for the International Reference Ionosphere (IRI)

Advances in space research, 2009-06, Vol.43 (11), p.1770-1775

2009

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A recurrent neural network approach to quantitatively studying solar wind effects on TEC derived from GPS; preliminary results

Annales geophysicae (1988), 2009-05, Vol.27 (5), p.2111-2125

2009

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Low solar activity variability and IRI 2007 predictability of equatorial Africa GPS TEC

Advances in space research, 2012-01, Vol.49 (2), p.316-326

2012

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Developing an ionospheric map for South Africa

Annales geophysicae (1988), 2010-07, Vol.28 (7), p.1431-1439

2010

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Evaluating the IRI topside model for the South African region: An overview of the modelling techniques

Advances in space research, 2009-09, Vol.44 (6), p.707-714

2009

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Evaluation of the STORM model storm-time corrections for middle latitude

Advances in space research, 2010-10, Vol.46 (8), p.1039-1046

2010

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Near-real time foF2 predictions using neural networks

Journal of atmospheric and solar-terrestrial physics, 2006-11, Vol.68 (16), p.1807-1818

2006

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A new global F2 peak electron density model for the International Reference Ionosphere (IRI)

Advances in space research, 2008-08, Vol.42 (4), p.645-658

2008

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Validation of University of New Brunswick Ionospheric Modeling Technique with ionosonde TEC estimation over South Africa

Advances in space research, 2009-09, Vol.44 (6), p.725-735

2009

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Comparisons of observed ionospheric F2 peak parameters with IRI-2001 predictions over South Africa

Journal of atmospheric and solar-terrestrial physics, 2009-02, Vol.71 (2), p.273-284

2009

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A neural network-based ionospheric model for Arecibo

Advances in space research, 2008-08, Vol.42 (4), p.776-781

2008

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Application of neural networks to South African GPS TEC modelling

Advances in space research, 2009-06, Vol.43 (11), p.1711-1720

2009

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On the global model for foF2 using neural networks

Radio science, 2005-12, Vol.40 (6), p.RS6011-n/a

2005

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Equatorial predictions from a new neural network based global foF2 model

Advances in space research, 2010-10, Vol.46 (8), p.1016-1023

2010

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Towards a GPS-based TEC prediction model for Southern Africa with feed forward networks

Advances in space research, 2009-07, Vol.44 (1), p.82-92

2009

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A new approach to modelling the daytime lower ionosphere at auroral latitudes

Advances in space research, 2004, Vol.34 (9), p.1943-1948

2004

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On the global short-term forecasting of the ionospheric critical frequency foF2 up to 5 hours in advance using neural networks

Radio science, 2005-12, Vol.40 (6), p.RS6012-n/a

2005

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The Economic Impact of Space Weather: Where Do We Stand?

Estimating the geoeffectiveness of halo CMEs from associated solar and IP parameters using neural networks

Investigating the performance of neural network backpropagation algorithms for TEC estimations using South African GPS data

Topside ionospheric vertical electron density profile reconstruction using GPS and ionosonde data: possibilities for South Africa

Present day challenges in understanding the geomagnetic hazard to national power grids

Solar and interplanetary precursors of geomagnetic storms in solar cycle 23

Statistical investigation of horizontal propagation of gravity waves in the ionosphere over Europe and South Africa

Observations of wave activity in the ionosphere over South Africa in geomagnetically quiet and disturbed periods

Progress towards a new global foF2 model for the International Reference Ionosphere (IRI)

A recurrent neural network approach to quantitatively studying solar wind effects on TEC derived from GPS; preliminary results

Low solar activity variability and IRI 2007 predictability of equatorial Africa GPS TEC

Developing an ionospheric map for South Africa

Evaluating the IRI topside model for the South African region: An overview of the modelling techniques

Evaluation of the STORM model storm-time corrections for middle latitude

Near-real time foF2 predictions using neural networks

A new global F2 peak electron density model for the International Reference Ionosphere (IRI)

Validation of University of New Brunswick Ionospheric Modeling Technique with ionosonde TEC estimation over South Africa

Comparisons of observed ionospheric F2 peak parameters with IRI-2001 predictions over South Africa

A neural network-based ionospheric model for Arecibo

Application of neural networks to South African GPS TEC modelling

On the global model for foF2 using neural networks

Equatorial predictions from a new neural network based global foF2 model

Towards a GPS-based TEC prediction model for Southern Africa with feed forward networks

A new approach to modelling the daytime lower ionosphere at auroral latitudes

On the global short-term forecasting of the ionospheric critical frequency foF2 up to 5 hours in advance using neural networks

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