https://vital.seals.ac.za/vital/access/manager/Index ${session.getAttribute("locale")} 5 https://vital.seals.ac.za/vital/access/manager/Repository/vital:49989 Wed 31 Aug 2022 12:31:44 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:28516 Wed 12 May 2021 19:46:51 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:50128 Wed 12 Jul 2023 19:15:47 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:73566 Thu 20 Jun 2024 16:01:55 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:34282 Thu 13 May 2021 07:18:46 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:28396 Thu 13 May 2021 02:06:20 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:38244 Thu 13 May 2021 00:04:36 SAST ]]> https://vital.seals.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://vital.seals.ac.za/vital/access/manager/Repository/vital:73149 Mon 24 Jun 2024 17:33:00 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:73614 Mon 24 Jun 2024 15:41:42 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:43448 Mon 05 Feb 2024 10:32:22 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:73595 Fri 21 Jun 2024 14:31:16 SAST ]]>