Jayapal V.Zain A.F.M.2024-05-282024-05-282016978151000000010.1109/PIERS.2016.77344252-s2.0-85006711890https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006711890&doi=10.1109%2fPIERS.2016.7734425&partnerID=40&md5=8cb596864385cf710730b6d06aa6d7achttps://oarep.usim.edu.my/handle/123456789/9424This paper investigates the capabilities of Neural Network (NN) application in estimating the missing ionospheric total electron content (TEC) data via interpolation and extrapolation methods. TEC is an important parameter that describes the state of the ionosphere. A multilayer feed-forward network with a back propagation algorithm is applied to estimate the TEC over Parit Raja (Lat. 1�52?N, Long. 103�06?E) an equatorial latitude station in Malaysia. The solar and magnetic indices, seasonal variation as well as diurnal variation are used as the input spaces in the NN to estimate the missing GPS TEC. The studies period is based on short term data during the medium solar activity period from 2005 to 2006. Normalized RMSE, RMSE and relative correction are computed for both methods to evaluate the capability of NN to interpolate and extrapolate the missing data. The results show that NN is able to interpolate the missing TEC data within the input range better than extrapolating the missing data outside the input range. The NN2 model finds hard to extrapolate the missing TEC data especially during the night hours, where the GPS TEC values are underestimated. Overall the relative correction of NN1 model is above 90% while for NN2 is below 90% for all ranges of missing rate. � 2016 IEEE.en-USBackpropagationBackpropagation algorithmsInterpolationIonosphereSolar energyDiurnal variationExtrapolation methodsExtrapolation techniquesIonospheric total electron contentMagnetic indicesMulti-layer feed-forward networksNeural network (nn)Seasonal variationExtrapolationConference Paper6956997734425