The neural network technique - 2: an ionospheric example illustrating its application

2004-01-01
Tulunay, Yurdanur
Tulunay, E
Senalp, ET
An example of modeling of Near Earth Space Processes by empoying the Neural Network Approach [Tulunay et al., Adv. Space Res., this current issue, 2003] is considered. The temporal and spatial forecasting of ionospheric critical frequency f(o)F2 values up to 24 h in advance by using the METUNN model is briefly covered. (C) 2003 COSPAR. Published by Elsevier Ltd. All rights reserved.
PATH TOWARD IMPROVED IONOSPHERE SPECIFICATION AND FORECAST MODELS

Suggestions

The neural network technique - 1: a general exposition
Tulunay, Yurdanur; Tulunay, E; Senalp, ET (Elsevier BV, 2004-01-01)
Near earth space processes are highly complex and nonlinear and mathematical modeling based on first physical principals is usually difficult or impossible. For such cases data driven modeling methods are recommended to be used in parallel with mathematical modeling approach. Highly non-linear processes in the near-earth space are advantageously dealt with using data-driven modeling techniques in the neural network (NN) approach. The only basic requirement for its application is the availability of represen...
Regional VTEC modeling with multivariate adaptive regression splines
Durmaz, Murat; Karslıoğlu, Mahmut Onur; Nohutcu, Metin (Elsevier BV, 2010-07-15)
Different algorithms have been proposed for the modeling of the ionosphere. The most frequently used method is based on the spherical harmonic functions achieving successful results for global modeling but not for the local and regional applications due to the bounded spherical harmonic representation. Irregular data distribution and data gaps cause also difficulties in the global modeling of the ionosphere. In this paper we propose an efficient algorithm with Multivariate Adaptive Regression Splines (MARS)...
The effect of geomagnetic activity changes on the ionospheric critical frequencies (foF2) at magnetic conjugate points
TİMOÇİN, ERDİNÇ; ÜNAL, İBRAHİM; Tulunay, Yurdanur; GÖKER, ÜMİT DENİZ (Elsevier BV, 2018-08-15)
In this work, we investigate the possible effects of geomagnetic activity on the ionospheric critical frequencies (foF2) in geomagnetic conjugate points. For this purpose, hourly foF2 data measured for the year 1976 from the ionosonde stations Akita, St. John's and Resolute Bay in the Northern hemisphere and their corresponding magnetic conjugate ionosonde stations Brisbane, Halley Bay and Scott Base in the Southern hemisphere are examined. Planetary geomagnetic activity "3h-K-p" indices are used as a geoma...
Non-parametric regional VTEC modeling with Multivariate Adaptive Regression B-Splines
Durmaz, Murat; Karslıoğlu, Mahmut Onur (Elsevier BV, 2011-11-01)
In this work Multivariate Adaptive Regression B-Splines (BMARS) is applied to regional spatio-temporal mapping of the Vertical Total Electron Content (VTEC) using ground based Global Positioning System (GPS) observations. BMARS is a non-parametric regression technique that utilizes compactly supported tensor product B-splines as basis functions, which are automatically obtained from the observations. The algorithm uses a scale-by-scale model building strategy that searches for B-splines at each scale fittin...
Forecasting magnetopause crossing locations by using Neural Networks
Tulunay, Yurdanur; Sibeck, DG; Senalp, ET; Tulunay, E (Elsevier BV, 2005-01-01)
Given the highly complex and nonlinear nature of Near Earth Space processes, mathematical modeling of these processes is usually difficult or impossible. In such cases, modeling methods involving Artificial Intelligence may be employed. We demonstrate that data driven models, such as the Neural Network based approach, shows promise in its ability to forecast or predict the behavior of these processes. In this paper, modeling studies for forecasting magnetopause crossing locations are summarized and a Neural...
Citation Formats
Y. Tulunay, E. Tulunay, and E. Senalp, “The neural network technique - 2: an ionospheric example illustrating its application,” PATH TOWARD IMPROVED IONOSPHERE SPECIFICATION AND FORECAST MODELS, pp. 988–992, 2004, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56308.