Forecasting total electron content maps by neural network technique

Tulunay, Ersin
Senalp, Erdem Turker
Radicella, Sandro Maria
Tulunay, Yurdanur
[ 1] Near-Earth space processes are highly nonlinear. Since the 1990s, a small group at the Middle East Technical University in Ankara has been working on a data-driven generic model of such processes, that is, forecasting and nowcasting of a near-Earth space parameter of interest. The model developed is called the Middle East Technical University Neural Network (METU-NN) model. The METU-NN is a data-driven neural network model of one hidden layer and several neurons. In order to understand more about the complex response of the magnetosphere and ionosphere to extreme solar events, we chose this time the series of space weather events in November 2003. Total electron content (TEC) values of the ionosphere are forecast during these space weather events. In order to facilitate an easier interpretation of the forecast TEC values, maps of TEC are produced by using the Bezier surface-fitting technique.


Improving the accuracy of the magnetic field integral equation with the linear-linear basis functions
Ergül, Özgür Salih (American Geophysical Union (AGU), 2006-07-18)
[ 1] Basis functions with linear variations are investigated in terms of the accuracy of the magnetic field integral equation (MFIE) and the combined-field integral equation (CFIE), on the basis of recent reports indicating the inaccuracy of the MFIE. Electromagnetic scattering problems involving conducting targets with arbitrary geometries, closed surfaces, and planar triangulations are considered. Specifically, two functions with linear variations along the triangulation edges in both tangential and norma...
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...
A fast and automatically paired 2-D direction-of-arrival estimation with and without estimating the mutual coupling coefficients
Filik, Tansu; Tuncer, Temel Engin (American Geophysical Union (AGU), 2010-06-26)
A new technique is proposed for the solution of pairing problem which is observed when fast algorithms are used for two-dimensional (2-D) direction-of-arrival (DOA) estimation. Proposed method is integrated with array interpolation for efficient use of antenna elements. Two virtual arrays are generated which are positioned accordingly with respect to the real array. ESPRIT algorithm is used by employing both the real and virtual arrays. The eigenvalues of the rotational transformation matrix have the angle ...
Uniform and nonuniform V-shaped planar arrays for 2-D direction-of-arrival estimation
Filik, T.; Tuncer, Temel Engin (American Geophysical Union (AGU), 2009-09-22)
In this paper, isotropic and directional uniform and nonuniform V-shaped arrays are considered for azimuth and elevation direction-of-arrival (DOA) angle estimation simultaneously. It is shown that the uniform isotropic V-shaped arrays (UI V arrays) have no angle coupling between the azimuth and elevation DOA. The design of the UI V arrays is investigated, and closed form expressions are presented for the parameters of the UI V arrays and nonuniform V arrays. These expressions allow one to find the isotropi...
Numerical evidence of spontaneous division of dissipative solitons in a planar gas discharge-semiconductor system
Rafatov, İsmail (AIP Publishing, 2019-09-01)
This work deals with the formation of patterns of spatially localized solitary objects in a planar semiconductor gas-discharge system with a high Ohmic electrode. These objects, known as dissipative solitons, are generated in this system in the form of self-organized current filaments, which develop from the homogeneous stationary state by the Turing bifurcation. The numerical model reveals, for the first time, evidence of spontaneous division of the current filaments in this system, similar to that observe...
Citation Formats
E. Tulunay, E. T. Senalp, S. M. Radicella, and Y. Tulunay, “Forecasting total electron content maps by neural network technique,” RADIO SCIENCE, pp. 0–0, 2006, Accessed: 00, 2020. [Online]. Available: