Harnessing evolutionary algorithms for enhanced characterization of ENSO events

The 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 the...

Full description

Saved in:
Bibliographic Details
Published inGenetic programming and evolvable machines Vol. 26; no. 1
Main Authors Abdulkarimova, Ulviya, Abarca-del-Rio, Rodrigo, Collet, Pierre
Format Journal Article
LanguageEnglish
Published New York Springer US 01.06.2025
Springer Nature B.V
Subjects
Artificial Intelligence
Biomedical Engineering and Bioengineering
Climate
Compilers
Computer Science
El Nino
Electrical Engineering
Environmental management
Evolutionary algorithms
Genetic algorithms
Interpreters
La Nina
Nonlinear feedback
Programming Languages
Programming Techniques
Software Engineering/Programming and Operating Systems
Southern Oscillation
La Niña
Symbolic regression
El Niño
Genetic algorithm
Genetic programming
Evolutionary algorithm
Stochastic optimization
Online AccessGet full text
ISSN1389-2576
1573-7632
DOI10.1007/s10710-024-09497-z

Cover

More Information
Summary:The 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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1389-2576
1573-7632
DOI:10.1007/s10710-024-09497-z