Neural network and wavelets in prediction of cosmic ray variability: The North Africa as study case
Abstract Since the Earth is permanently bombarded with energetic cosmic rays particles, cosmic ray flux has been monitored by ground based neutron monitors for decades. In this work an attempt is made to investigate the decomposition and reconstructions provided by Morlet wavelet technique, using data series of cosmic rays variabilities, then to constitute from this wavelet analysis an input data base for the neural network system with which we can then predict decomposition coefficients and all related parameters for other points. Thus the latter are used for the recomposition step in which the plots and curves describing the relative cosmic rays intensities are obtained in any points on the earth in which we do not have any information about cosmic rays intensities. Although neural network associated with wavelets are not frequently used for cosmic rays time series, they seems very suitable and are a good choice to obtain these results. In fact we have succeeded to derive a very useful tool to obtain the decomposition coefficients, the main periods for each point on the Earth and on another hand we have now a kind of virtual NM for these locations like North Africa countries, Maroc, Algeria, Tunisia, Libya and Cairo. We have found the aspect of very known 11-years cycle: T1, we have also revealed the variation type of T2 and especially T3 cycles which seem to be induced by particular Earth's phenomena.