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

Habarulema, John B; McKinnell, Lee-Anne; Opperman, Ben D L

Title: Towards a GPS-based TEC prediction model for Southern Africa with feed forward networks
Creator: Habarulema, John B
Creator: McKinnell, Lee-Anne
Creator: Opperman, Ben D L
Date Issued: 2009
Date: 2009
Type: text
Type: Article
Identifier: vital:6806
Identifier: http://hdl.handle.net/10962/d1004192
Description: In this paper, first results from a national Global Positioning System (GPS) based total electron content (TEC) prediction model over South Africa are presented. Data for 10 GPS receiver stations distributed through out the country were used to train a feed forward neural network (NN) over an interval of at most five years. In the NN training, validating and testing processes, five factors which are well known to influence TEC variability namely diurnal variation, seasonal variation, magnetic activity, solar activity and the geographic position of the GPS receivers were included in the NN model. The database consisted of 1-min data and therefore the NN model developed can be used to forecast TEC values 1 min in advance. Results from the NN national model (NM) were compared with hourly TEC values generated by the earlier developed NN single station models (SSMs) at Sutherland (32.38°S, 20.81°E) and Springbok (29.67°S, 17.88°E), to predict TEC variations over the Cape Town (33.95°S, 18.47°E) and Upington (28.41°S, 21.26°E) stations, respectively, during equinoxes and solstices. This revealed that, on average, the NM led to an improvement in TEC prediction accuracy compared to the SSMs for the considered testing periods.
Format: 21 pages
Format: pdf
Language: English
Relation: Habarulema, John Bosco and McKinnell, Lee-Anne and Opperman, B. D. L. (2009) Towards a GPS-based TEC prediction model for Southern Africa with feed forward networks. Advances in Space Research, 44 (1). pp. 82-92. Available: http://dx.doi.org/10.1016/j.asr.2009.02.016

Hits: 974
Visitors: 1000
Downloads: 170

Collections

McKinnell, Lee-Anne (Dr)

Habarulema, John Bosco (Dr)

RU Department of Physics and Electronics

SDG 09. Industry, Innovation and Infrastructure

		Thumbnail	File	Description	Size	Format
View Details Download			SOURCEPDF	PDF	796 KB	Adobe Acrobat PDF	View Details Download