Harnessing evolutionary algorithms for enhanced characterization of ENSO events

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dc.contributor.authorAbdulkarimova, Ulviya
dc.contributor.authorAbarca-del-Rio, Rodrigo
dc.contributor.authorCollet, Pierre
dc.date.accessioned2025-01-21T15:01:41Z
dc.date.available2025-01-21T15:01:41Z
dc.date.issued0025-01
dc.descriptionINDEXACION SCOPUS
dc.description.abstractThe El Niño-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 Niño and La Niña events, spanning their various intensities. By analyzing data from the Oceanic Niño 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. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
dc.identifier.doi10.1007/s10710-024-09497-z
dc.identifier.issn13892576
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/63119
dc.language.isoen
dc.publisherSpringer
dc.subjectEl Niño; Evolutionary algorithm; Genetic algorithm; Genetic programming; La Niña; Stochastic optimization; Symbolic regression
dc.titleHarnessing evolutionary algorithms for enhanced characterization of ENSO events
dc.typeArtículo
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