https://commons.ufh.ac.za/vital/access/manager/Index ${session.getAttribute("locale")} 5 https://commons.ufh.ac.za/vital/access/manager/Repository/vital:5560 Wed 12 May 2021 19:50:08 SAST ]]> https://commons.ufh.ac.za/vital/access/manager/Repository/vital:5555 Wed 12 May 2021 17:41:38 SAST ]]> https://commons.ufh.ac.za/vital/access/manager/Repository/vital:5434 Wed 12 May 2021 16:43:54 SAST ]]> https://commons.ufh.ac.za/vital/access/manager/Repository/vital:5472 Thu 13 May 2021 08:08:05 SAST ]]> https://commons.ufh.ac.za/vital/access/manager/Repository/vital:5455 Thu 13 May 2021 00:45:07 SAST ]]> https://commons.ufh.ac.za/vital/access/manager/Repository/vital:42937 4. The modeling methods used in the study were artificial neural network (ANN), linear regression (LR) and polynomial functions. The approach taken was to first test the modeling techniques on a single station before expanding the study to cover the regional aspect. The single station modeling was developed based on ionosonde data over Grahamstown. The inputs for the model which related to seasonal variation, diurnal variation, geomagnetic activity and solar activity were considered. For the geomagnetic activity, three indices namely; the symmetric disturbance in the horizontal component of the Earth’s magnetic field (SYM − H), the Auroral Electrojet (AE) index and local geomagnetic index A, were included as inputs. The performance of a single station model revealed that, of the three geomagnetic indices, SYM − H index has the largest contribution of 41% and 54% based on ANN and LR techniques respectively. The average correlation coefficients (R) for both ANN and LR models was 0.8, when validated during the selected storms falling within the period of model development. When validated using storms that fall outside the period of model development, the model gave R values of 0.6 and 0.5 for ANN and LR respectively. In addition, the GPS total electron content (TEC) derived measurements were used to estimate foF2 data. This is because there are more GPS receivers than ionosonde locations and the utilisation of this data increases the spatial coverage of the regional model. The estimation of foF2 from GPS TEC was done at GPS-ionosonde co-locations using polynomial functions. The average R values of 0.69 and 0.65 were obtained between actual and derived _foF2 over the co-locations and other GPS stations respectively. Validation of GPS TEC derived foF2 with RO data over regions out of ionospheric pierce points coverage with respect to ionosonde locations gave R greater than 0.9 for the selected storm period of 4-8 August 2011. The regional storm-time model was then developed based on the ANN technique using the four South African ionosonde stations. The maximum and minimum R values of 0.6 and 0.5 were obtained over ionosonde and GPS locations respectively. This model forms the basis towards the regional ionospheric storm-time index.]]> Mon 31 May 2021 15:12:57 SAST ]]> https://commons.ufh.ac.za/vital/access/manager/Repository/vital:49993 Mon 10 Oct 2022 08:47:40 SAST ]]>