Highlights An ANN based model has been suggested for f o F 2 values over EIA region stations during 1958–2008. The developed model predicts f o F 2 with a root mean square error of less than 1 MHz for the unseen testing data. The model may be used for practical purposes over Asian longitude sector, particularly for Pakistan.

    Abstract The critical frequency of F 2 -layer in the Equatorial Ionization Anomaly (EIA) region is one of the most challenging ionospheric characteristics to model. We illustrate the efforts to model monthly hourly medians of f o F 2 using Artificial Neural Network (ANN) over five EIA crest stations between 23.1° N – 30.5° N of Eastern longitudes during 1958–2008 (as per data availability per station). The ANN based model was designed to obtain f o F 2 values as output by inputting seven parameters defining diurnal (hour), seasonal (month), geographical (latitude & longitude) and solar cycle (year, F10.7 & F10.7P indices) dependency of f o F 2 . With this input space, the model uses a hidden layer of 96 neurons to generate output f o F 2 , producing a mean squared error of less than 1.0 MHz 2 . Additionally, the convergence of the model improved on introducing derived solar radio flux (F10.7P) by 6.0%. After training and validation, the model predicts f o F 2 values over the locations under study for unseen testing data of 1989, 1998 & 2008 as a representation of high, moderate and low solar activity, respectively. Our ANN based climatological model gave a Root Mean Square Error (RMSE) of 0.956 MHz and goodness of fit, r 2 =0.929 for the testing data. Data-model comparison with ANN model is better than the International Reference Ionosphere (IRI) estimations of f o F 2 with CCIR (IRIC) and URSI (IRIU) coefficients. IRIC and IRIU produced RMSE of 1.901 MHz ( r 2 = 0.732 ) and 1.926 MHz ( r 2 =0.716) for the same testing data set, respectively. The designed ANN based model successfully captures the climatological features of f o F 2 in the EIA region. After necessary modifications, the model may be applied for predicting daily hourly values, particularly in the Asian-longitudinal range over Pakistan, in future.


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    Titel :

    Modelling of f o F 2 using artificial neural network over Equatorial Ionization Anomaly (EIA) region stations


    Beteiligte:

    Erschienen in:

    Advances in Space Research ; 72 , 12 ; 5539-5550


    Erscheinungsdatum :

    2022-09-15


    Format / Umfang :

    12 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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