Neural network-based prediction techniques for global modeling of M(3000)F2 ionospheric parameter

Oyeyemi, E O; McKinnell, Lee-Anne; Poole, Allon W V

Title: Neural network-based prediction techniques for global modeling of M(3000)F2 ionospheric parameter
Creator: Oyeyemi, E O
Creator: McKinnell, Lee-Anne
Creator: Poole, Allon W V
Date Issued: 2007
Date: 2007
Type: text
Type: Article
Identifier: vital:6803
Identifier: http://hdl.handle.net/10962/d1004166
Description: In recent times neural networks (NNs) have been employed to solve many problems in ionospheric predictions. This paper illustrates a new application of NNs in developing a global model of the ionospheric propagation factor M(3000)F2. NNs were trained with daily hourly values of M(3000)F2 from various ionospheric stations spanning the period 1964–1986 with the following temporal and spatial input parameters: Universal Time, geographic latitude, magnetic inclination, magnetic declination, solar zenith angle, day of the year, A16 index (a 2-day running mean of the 3-h planetary magnetic ap index), R2 index (a 2-month running mean of sunspot number), and the angle of meridian relative to the subsolar point. The performance of the NNs was verified by comparing the predicted values of M(3000)F2 with observed values from a few selected ionospheric stations and the IRI (International Reference Ionosphere) model (CCIR M(3000)F2 model) predicted values. The results obtained compared favourably with the IRI model. Based on the error differences, the result obtained justifies the potential of the NN technique for the predictions of M(3000)F2 values on a global scale.
Format: 12 pages
Format: pdf
Language: English
Relation: Oyeyemi, Elijah Oyedola and McKinnell, Lee-Anne and Poole, A.W.V. (2007) Neural network-based prediction techniques for global modeling of M(3000)F2 ionospheric parameter. Advances in Space Research, 39 (5). pp. 643-650. Available: http://dx.doi.org/10.1016/j.asr.2006.09.038

Hits: 872
Visitors: 880
Downloads: 128

Collections

Poole, Allon (Prof)

McKinnell, Lee-Anne (Dr)

RU Department of Physics and Electronics

SDG 09. Industry, Innovation and Infrastructure

		Thumbnail	File	Description	Size	Format
View Details Download			SOURCEPDF	PDF	617 KB	Adobe Acrobat PDF	View Details Download