Nonlinear Processes in Geophysics www.nonlin-processes-geophys.net 1023-5809 1607-7946 9 5/6 2002 10.5194/npg-9-477-2002 http://www.nonlin-processes-geophys.net/9/477/2002/ http://www.nonlin-processes-geophys.net/9/477/2002/npg-9-477-2002.html http://www.nonlin-processes-geophys.net/9/477/2002/npg-9-477-2002.pdf 477 486 0000-00-00 Neural-network-based prediction techniques for single station modeling and regional mapping of the <I>fo</I>F2 and M(3000)F2 ionospheric characteristics T. D. Xenos Aristotelian University of Thessaloniki, Dept of Electrical and Computers Eng., 54006 Thessaloniki, Greece In this work, Neural-Network-based single-station hourly daily <i>f</i>oF2 and M(3000)F2 modelling of 15 European ionospheric stations is investigated. The data used are neural networks and hourly daily values from the period 1964- 1988 for training the neural networks and from the period 1989-1994 for checking the prediction accuracy. Two types of models are presented for the F2-layer critical frequency prediction and two for the propagation factor M(3000)F2. The first <i>f</i>oF2 model employs the E-layer local noon calculated daily critical frequency <i>(f</i>oE₁₂) and the local noon F2- layer critical frequency of the previous day. The second <i>f</i>oF2 model, which introduces a new regional mapping technique, employs the Juliusruh neural network model and uses the E-layer local noon calculated daily critical frequency <i>(f</i>oE₁₂), and the previous day F2-layer critical frequency measured at Juliusruh at noon. The first M(3000)F2 model employs the E-layer local noon calculated daily critical frequency <i>(f</i>oE₁₂), its ± 3 h deviations and the local noon cosine of the solar zenith angle (cos <font face="Symbol">c</font>₁₂). The second model, which introduces a new M(3000)F2 mapping technique, employs Juliusruh neural network model and uses the E-layer local noon calculated daily critical frequency <i>(f</i>oE₁₂), and the previous day F2-layer critical frequency measured at Juliusruh at noon.