Four-layer spherical self-organized maps neural networks trained by recirculation to follow the phase evolution of a nearly four-year rainfall signal.

2019-06-132019-06-132019-01http://hdl.handle.net/20.500.12272/3690This work is intended to organize a big set of time series of rainfall reanalysis built on the Fourier harmonic that corresponds to the 4.8year cycle of variability. To do that a self-organized map is implemented in four spherical layers trained by recirculation. The methodology is shortly described. It is used to organize time series on grid point around the Earth to follow the phase evolution of the signal. The phase and amplitude are the main criterion for organization. It is shown how the successive layers contain more general abstractions, their representativeness around the Globe and in regional scale. The main objective is to show how to use the neural network tool to follow the phase evolution of the signal around the Globe. It is described as an anomaly with highest amplitude in the central Pacific Ocean, this evolution and return after 4.8 years.application/pdfenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/Neural Network, Spherical Self-Organized Maps, Recirculation, Signal Analysis. Phase Evolution, Rainfall Reanalysis, Climate VariabilityFour-layer spherical self-organized maps neural networks trained by recirculation to follow the phase evolution of a nearly four-year rainfall signal.info:eu-repo/semantics/articleNadu Tamil Editorial : ICT AcademyAtribución - No comercial - Compartir Igual (by-nc-sa): No se permite un uso comercial de la obra original ni de las posibles obras derivadas, la distribución de las cuales se debe hacer con una licencia igual a la que regula la obra original. Esta licencia no es una licencia libre.Attribution-NonCommercial-NoDerivatives 4.0 Internacional10.21917/ijsc.2019.0262