首页|Harnessing evolutionary algorithms for enhanced characterization of ENSO events
Harnessing evolutionary algorithms for enhanced characterization of ENSO events
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NETL
NSTL
Springer Nature
The El Nino-Southern Oscillation (ENSO) significantly influences the complexity and variability of the global climate system, driving its variability. ENSO events' irregularity and unpredictability arise from intricate ocean-atmosphere interactions and nonlinear feedback mechanisms, complicating their prediction of timing, intensity, and geographic impacts. This study applies Genetic Programming and Genetic Algorithms within the EASEA (EAsy Specification of Evolutionary Algorithms) Evolutionary Algorithms (EA) framework to develop a repository of symbolic equations for El Nino and La Nina events, spanning their various intensities. By analyzing data from the Oceanic Nino Index, this approach yields equation-based characterizations of ENSO events. This methodology not only enhances ENSO characterization strategies but also contributes to expanding the use of EAs in climate event analysis. The resulting equations have the potential to offer insights beyond academia, benefiting education, climate policy, and environmental management. This highlights the importance of ongoing refinement, validation, and exploration in these fields through EAs.
El NinoLa NinaGenetic programmingGenetic algorithmEvolutionary algorithmSymbolic regressionStochastic optimization
Azerbaijan State Oil and Industry University (ASOIU)/French-Azerbaijani University (UFAZ), Baku, Azerbaijan||ICUBE Laboratory, University of Strasbourg, Strasbourg, France
Department of Geophysics, Faculty of Physics and Mathematics, Universidad de Concepcion, Concepcion, Chile
ITISB, Faculdad de Ingeniera, Universidad Andres Bello, Vina del Mar, Chile
NETL
NSTL
Springer Nature
