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Harnessing evolutionary algorithms for enhanced characterization of ENSO events

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Abstract

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.

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Data availability

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Notes

  1. https://easea.unistra.fr

References

  1. M. Davey, El Niño and the Souther Oscillation: Multiscale variability and global and regional impacts. Q. J. R. Meteorol. Soc. (2002). https://doi.org/10.1256/003590002320373355

    Article  Google Scholar 

  2. S. Yang, Z. Li, J.-Y. Yu, X. Hu, W. Dong, S. He, El Niño-Southern Oscillation and its impact in the changing climate. Natl. Sci. Rev. (2018). https://doi.org/10.1093/nsr/nwy046

    Article  Google Scholar 

  3. R. Abarca-del-Rio, D. Gambis, D. Salstein, Interannual signals in length of day and atmospheric angular momentum. Ann. Geophys. 18, 347–364 (2000). https://doi.org/10.1007/s00585-000-0347-9

    Article  Google Scholar 

  4. B. Kolaczek, J. Nastula, D. Salstein, El Nino-related variations in atmosphere-polar motion interactions. J. Geodyn. 36(3), 397–406 (2003). https://doi.org/10.1016/S0264-3707(03)00058-9

    Article  Google Scholar 

  5. T. Delcroix, S.L.L. Michel, D. Swingedouw, B. Malaizé, A.-L. Daniau, R. Abarca-del-Rio, T. Caley, A.-M. Sémah, Clarifying the role of ENSO on easter island precipitation changes: potential environmental implications for the last millennium. Paleoceanogr. Paleoclimatol. 37(12), 2022–004514 (2022)

    Article  Google Scholar 

  6. G. Dufrénot, W. Ginn, M. Pourroy, ENSO Climate Patterns on Global Economic Conditions. https://doi.org/10.21203/rs.3.rs-2827605/v1

  7. N. Dai, P.A. Arkin, Twentieth century ENSO-related precipitation mean states in twentieth century reanalysis, reconstructed precipitation and CMIP5 models. Clim. Dyn. 48(9–10), 3061–3083 (2017)

    Article  Google Scholar 

  8. O. Muza, El Nino-Southern Oscillation influences on food security. J. Sustain. Dev. 10(5), 268–279 (2017). https://doi.org/10.5539/jsd.v10n5p268

    Article  Google Scholar 

  9. Z.W. Kundzewicz, M. Szwed, I. Pińskwar, Climate variability and floods-a global review. Water 11(7), 1399 (2019). https://doi.org/10.3390/w11071399

    Article  Google Scholar 

  10. I. Fustos, R. Abarca-del-Rio, P. Moreno-Yaeger, M. Somos-Valenzuela, Rainfall-induced landslides forecast using local precipitation and global climate indexes. Nat. Hazards 102, 115–131 (2020). https://doi.org/10.1007/s11069-020-03827-5

    Article  Google Scholar 

  11. H. Yin, Z. Wu, H.J. Fowler, S. Blenkinsop, H. He, Y. Li, The combined impacts of ENSO and IOD on global seasonal droughts. Atmosphere 13, 1673 (2022). https://doi.org/10.3390/atmos13101673

    Article  Google Scholar 

  12. G.G. Nobre, S. Muis, T.I. Veldkamp, P.J. Ward, Achieving the reduction of disaster risk by better predicting impacts of El Niño and La Niña. Prog. Disaster Sci. 2, 100022 (2019). https://doi.org/10.1016/j.pdisas.2019.100022

    Article  Google Scholar 

  13. S.I. An, A review of interdecadal changes in the nonlinearity of the El Nino-Southern Oscillation. Theor. Appl. Climatol. 97, 29–40 (2009). https://doi.org/10.1007/s00704-008-0085-8

    Article  Google Scholar 

  14. H.F. Astudillo, F.A. Borotto, R. Abarca-del Rio, Embedding reconstruction methodology for short time series—application to large El Niño events. Nonlinear Process. Geophys. 17, 753–764 (2010). https://doi.org/10.5194/npg-17-753-2010

    Article  Google Scholar 

  15. S.T. Ogunjo, I.A. Fuwape, Nonlinear characterization and interaction in teleconnection patterns. Adv. Space Res. 65(12), 2723–2732 (2020). https://doi.org/10.1016/j.asr.2020.02.006

    Article  Google Scholar 

  16. O. Alizadeh, A review of ENSO teleconnections at present and under future global warming. Wiley Interdiscip. Rev. Clim. Change 15(1), 861 (2024). https://doi.org/10.1002/wcc.861

    Article  Google Scholar 

  17. S. Das, R. Bhardwaj, V. Duhoon, Chaotic dynamics of recharge-discharge El Nino-Southern Oscillation (ENSO) model. Eur. Phys. J. Spec. Top. 232(1), 217–230 (2023)

    Article  Google Scholar 

  18. A.R. Lima, A.J. Cannon, W.W. Hsieh, Nonlinear regression in environmental sciences using extreme learning machines: a comparative evaluation. Environ. Model. Softw. 73, 175–188 (2015). https://doi.org/10.1016/j.envsoft.2015.08.002

    Article  Google Scholar 

  19. P. Nooteboom, Q. Feng, C. López, E. Hernández-García, H. Dijkstra, Using network theory and machine learning to predict El Niño. Earth Syst. Dyn. Discuss. 9(3), 969–83 (2018). https://doi.org/10.5194/esd-2018-13

    Article  Google Scholar 

  20. H.A. Dijkstra, P. Petersik, E. Hernández-García, C. López, The application of machine learning techniques to improve El Niño prediction skill. Front. Phys. 7, 153 (2019). https://doi.org/10.3389/fphy.2019.00153

    Article  Google Scholar 

  21. Y. Tang, A. Duan, Using deep learning to predict the East Asian summer monsoon. Environ. Res. Lett. 16(12), 124006 (2021). https://doi.org/10.1088/1748-9326/ac34bc

    Article  Google Scholar 

  22. S. Liu, C. Ding, F. Jiang, Y. Wang, B. Yin, Auto-weighted multi-view learning for semi-supervised graph clustering. Neurocomputing 362, 19–32 (2019). https://doi.org/10.1016/j.neucom.2019.07.011

    Article  Google Scholar 

  23. M. Saha, R. Nanjundiah, Prediction of ENSO and EQUINOO indices during June to September using deep learning method. Meteorol. Appl. 27, e1826 (2019). https://doi.org/10.1002/met.1826

    Article  Google Scholar 

  24. G.-G. Wang, H. Cheng, Y. Zhang, H. Yu, ENSO analysis and prediction using deep learning: a review. Neurocomputing (2022)

  25. A. Alvarez, P. Vélez, A. Orfila, G. Vizoso, J. Tintoré (2002) Evolutionary computation for climate and ocean forecasting: “El Niño forecasting”. In: N.C. Fiemming, S. Vallerga, N. Pinardi, H.W.A. Behrens, G. Manzella, D. Prandle, J.H. Stei (eds.) Opertional Oceanography. Elsevier Oceanography Series, vol. 66, pp. 489–494 . https://doi.org/10.1016/S0422-9894(02)80055-1

  26. I. De Falco, A. Della Cioppa, E. Tarantino, A genetic programming system for time series prediction and its application to El Niño forecast, in Soft Computing: Methodologies and Applications. ed. by F. Hoffmann, M. Köppen, F. Klawonn, R. Roy (Springer, Berlin, 2005), pp.151–162

    Chapter  Google Scholar 

  27. K. Stanisławska, K. Krawiec, Z. Kundzewicz, Modeling global temperature changes with genetic programming. Comput. Math. Appl. 64, 3717–3728 (2012). https://doi.org/10.1016/j.camwa.2012.02.049

    Article  Google Scholar 

  28. Y. Wang, Y. Zhang, G.-G. Wang, Forecasting ENSO using convolutional lstm network with improved attention mechanism and models recombined by genetic algorithm in CMIP5/6. Inf. Sci. 642, 119106 (2023). https://doi.org/10.1016/j.ins.2023.119106

    Article  Google Scholar 

  29. H.F. Astudillo, R. Abarca-del Rio, F.A. Borotto, Long-term potential nonlinear predictability of El Niño-La Niña events. Clim. Dyn. 49, 131 (2017). https://doi.org/10.1007/00382-016-3330-1

    Article  Google Scholar 

  30. R.H. Zhang, C. Gao, L. Feng, Recent ENSO evolution and its real-time prediction challenges. Natl. Sci. Rev. 9(4), 052 (2022)

    Article  Google Scholar 

  31. H. Wang, Y. Dai, S. Yang, T. Li, J. Luo, B. Sun, M. Duan, J. Ma, Z. Yin, Y. Huang, Predicting climate anomalies: a real challenge. Atmos. Ocean. Sci. Lett. 15(1), 100115 (2022)

    Article  Google Scholar 

  32. J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Evolution (MIT Press, Cambridge, 1992)

    Google Scholar 

  33. W. Banzhaf, P. Nordin, R. Keller, F. Francone, Genetic programming: an introduction on the automatic evolution of computer programs and its applications (1998)

  34. J.R. Koza, Genetic Programming IV: Routine Human-Competitive Machine Intelligence (Kluwer Academic Publishers, New York, 2003)

    Google Scholar 

  35. J.R. Koza, R. Poli, (2005) Genetic Programming, pp. 127–164. https://doi.org/10.1007/0-387-28356-0_5

  36. A. Brindle, Genetic algorithms for function optimisation. Technical Report TR81-2, Dept. of Computer Science, University of Alberta, Edmonton (1981)

  37. D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, New York, 1989)

    Google Scholar 

  38. J.H. Holland, Adaptation in Natural and Artificial Systems (MIT Press, Cambridge, 1992)

    Book  Google Scholar 

  39. N.L. Cramer, A representation for the adaptive generation of simple sequential programs, in Proceedings of the First International Conference on Genetic Algorithms, vol. 183, p. 187 (1985)

  40. M.A. Lones, S.L. Smith, in W. Banzhaf, P. Machado, M. Zhang (eds.) Evolutionary Machine Learning in Medicine, pp. 591–609. Springer, Singapore (2024). https://doi.org/10.1007/978-981-99-3814-8_20

  41. Q.U. Ain, H. Al-Sahaf, B. Xue, M. Zhang, Genetic programming for automatic skin cancer image classification. Expert Syst. Appl. 197, 116680 (2022)

    Article  Google Scholar 

  42. M. O’Neill, A. Brabazon, in W. Banzhaf, P. Machado, M. Zhang (eds.) Evolutionary Machine Learning in Finance, pp. 695–713. Springer, Singapore (2024). https://doi.org/10.1007/978-981-99-3814-8_24

  43. S.-H. Chen, Genetic Algorithms and Genetic Programming in Computational Finance (Springer, New York, 2012)

    Google Scholar 

  44. N. Nedjah, L.d.M. Mourelle, in N. Nedjah, L.d.M. Mourelle, A. Abraham (eds.) Evolutionary Digital Circuit Design Using Genetic Programming, pp. 147–171. Springer, Berlin (2006). https://doi.org/10.1007/3-540-32498-4_7

  45. J.R. Koza, F.H. Bennett, D. Andre, M.A. Keane, Evolutionary design of analog electrical circuits using genetic programming, in Adaptive Computing in Design and Manufacture. ed. by I.C. Parmee (Springer, London, 1998), pp.177–192

    Chapter  Google Scholar 

  46. J.E. Batista, S. Silva, in W. Banzhaf, P. Machado, M. Zhang (eds.) Evolutionary Machine Learning in Environmental Science, pp. 563–590. Springer, Singapore (2024). https://doi.org/10.1007/978-981-99-3814-8_19

  47. J.E. Batista, S. Silva, Evolving a cloud-robust water index with genetic programming, in Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 55–56 (2022)

  48. X. Wen, X. Zhang, H. Xing, G. Ye, H. Li, Y. Zhang, H. Wang, An improved genetic algorithm based on reinforcement learning for aircraft assembly scheduling problem. Comput. Ind. Eng. 193, 110263 (2024). https://doi.org/10.1016/j.cie.2024.110263

    Article  Google Scholar 

  49. L. He, W. Wang, Design optimization of public building envelope based on multi-objective quantum genetic algorithm. J. Build. Eng. 91, 109714 (2024). https://doi.org/10.1016/j.jobe.2024.109714

    Article  Google Scholar 

  50. A. Sohail, Genetic algorithms in the fields of artificial intelligence and data sciences. Ann. Data Sci. 10 (2021). https://doi.org/10.1007/s40745-021-00354-9

  51. M. Muslim, D. Yosza, H. Javed, A. Alamsyah, J. Unjung, W. Abror, D.A.A. Pertiwi, T. Mustaqim, An ensemble stacking algorithm to improve model accuracy in bankruptcy prediction. J. Data Sci. Intell. Syst. 2, 1–2 (2023). https://doi.org/10.47852/bonviewJDSIS3202655

    Article  Google Scholar 

  52. H. Maier, S. Razavi, Z. Kapelan, L. Matott, J. Kasprzyk, B. Tolson, Introductory overview: optimization using evolutionary algorithms and other metaheuristics. Environ. Model. Softw. 114, 1–2 (2018). https://doi.org/10.1016/j.envsoft.2018.11.018

    Article  Google Scholar 

  53. ...H.R. Maier, Z. Kapelan, J. Kasprzyk, J. Kollat, L.S. Matott, M.C. Cunha, G.C. Dandy, M.S. Gibbs, E. Keedwell, A. Marchi, A. Ostfeld, D. Savic, D.P. Solomatine, J.A. Vrugt, A.C. Zecchin, B.S. Minsker, E.J. Barbour, G. Kuczera, F. Pasha, A. Castelletti, M. Giuliani, P.M. Reed, Evolutionary algorithms and other metaheuristics in water resources: current status, research challenges and future directions. Environ. Model. Softw. 62, 271–299 (2014). https://doi.org/10.1016/j.envsoft.2014.09.013

    Article  Google Scholar 

  54. A.S. Leon, L. Bian, Y. Tang, Comparison of the genetic algorithm and pattern search methods for forecasting optimal flow releases in a multi-storage system for flood control. Environ. Model. Softw. 145, 105198 (2021). https://doi.org/10.1016/j.envsoft.2021.105198

    Article  Google Scholar 

  55. C.-P. Tung, T.-Y. Lee, Y.-C.E. Yang, Y.-J. Chen, Application of genetic programming to project climate change impacts on the population of formosan landlocked salmon. Environ. Model. Softw. 24(9), 1062–1072 (2009). https://doi.org/10.1016/j.envsoft.2009.02.012

    Article  Google Scholar 

  56. D. Carreres-Prieto, J. Ybarra-Moreno, J.T. García, J.F. Cerdán-Cartagena, A comparative analysis of neural networks and genetic algorithms to characterize wastewater from led spectrophotometry. J. Environ. Chem. Eng. 11(3), 110219 (2023). https://doi.org/10.1016/j.jece.2023.110219

    Article  Google Scholar 

  57. A. Danandeh Mehr, V. Nourani, E. Kahya, B. Hrnjica, A.M.A. Sattar, Z.M. Yaseen, Genetic programming in water resources engineering: a state-of-the-art review. J. Hydrol. 566, 643–667 (2018). https://doi.org/10.1016/j.jhydrol.2018.09.043

    Article  Google Scholar 

  58. A. Danandeh Mehr, An improved gene expression programming model for streamflow forecasting in intermittent streams. J. Hydrol. 563, 669–678 (2018). https://doi.org/10.1016/j.jhydrol.2018.06.049

    Article  Google Scholar 

  59. GGWeather.com: Oceanic Niño Index (ONI). https://ggweather.com/enso/oni.htm Accessed 2024-04-01

  60. B. Huang, V.F. Banzon, E. Freeman, J. Lawrimore, W. Liu, T.C. Peterson, T.M. Smith, P.W. Thorne, S.D. Woodruff, H.-M. Zhang, Extended reconstructed sea surface temperature version 4 (ersst.v4). part i: Upgrades and intercomparisons. J. Clim. 28(3), 911–930 (2015). https://doi.org/10.1175/JCLI-D-14-00006.1

    Article  Google Scholar 

  61. R. Ding, Y.-H. Tseng, E. Di Lorenzo, L. Shi, J. Li, J.-Y. Yu, C. Wang, C. Sun, J.-J. Luo, K.-J. Ha, Z.-Z. Hu, F. Li, Multi-year el niño events tied to the north pacific oscillation. Nat. Commun. 13 (2022). https://doi.org/10.1038/s41467-022-31516-9

  62. P. Collet, J.P. Rennard, Stochastic optimization algorithms. Handbook of Research on Nature Inspired Computing for Economics and Management (2007)

  63. P. Collet, E. Lutton, M. Schoenauer, J. Louchet, Take it easea, in Parallel Problem Solving from Nature PPSN VI. ed. by M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J.J. Merelo, H.-P. Schwefel (Springer, Berlin, 2000), pp.891–901

    Chapter  Google Scholar 

  64. U. Abdulkarimova, A. Leonteva, A. Jeannin-Girardon, P. Collet, The PARSEC machine: a non-Newtonian supra-linear supercomputer. Azerbaijan J. High Perform. Comput. 2, 122–140 (2019). https://doi.org/10.32010/26166127.2019.2.2.122.140

    Article  Google Scholar 

  65. O. Maitre, N. Lachiche, P. Clauss, L. Baumes, A. Corma, P. Collet, Efficient parallel implementation of evolutionary algorithms on GPGPU cards, in Euro-Par (2009)

  66. O. Maitre, S. Querry, N. Lachiche, P. Collet, EASEA parallelization of tree-based genetic programming, in al, F. (ed.) IEEE CEC 2010, pp. 1–8. IEEE, New York (2010). https://doi.org/10.1109/CEC.2010.5586258

  67. O. Maitre, GPGPU for evolutionary algorithms. Ph.D. thesis, University of Strasbourg (2011)

  68. P. Collet, F. Kruger, O. Maitre, Automatic parallelization of EC on GPGPUs and clusters of GPGPU machines with EASEA and EASEA-CLOUD, pp. 35–59 (2013). https://doi.org/10.1007/978-3-642-37959-8_3

  69. O. Maitre, F. Kruger, D. Sharma, S. Querry, N. Lachiche, P. Collet, Parallelizing Evolutionary Algorithms on GPGPU Cards with the EASEA Platform, pp. 301–319 (2017). https://doi.org/10.1002/9781119332015.ch15

  70. H.-L. Ren, B. Lu, J. Wan, B. Tian, P. Zhang, Identification Standard for ENSO Events and Its Application to Climate Monitoring and Prediction in China. J. Meteorol. Res. 32(6), 923–936 (2018). https://doi.org/10.1007/s13351-018-8078-6

    Article  Google Scholar 

  71. F. Alvial Vásquez, R. Abarca-del-Rio, A. Ávila, High-resolution precipitation gridded dataset on the south-central zone (\(34^\circ s-41 ^\circ\) s) of chile. Front. Earth Sci. 8 (2020) https://doi.org/10.3389/feart.2020.519975

  72. E.R. Girden, ANOVA: Repeated Measures, vol. 84 (Sage, New York, 1992)

    Book  Google Scholar 

  73. El Niño and La Niña years and intensities. https://ggweather.com/enso/oni.htm

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Acknowledgements

The authors sincerely thank the referees for their thoughtful and thorough reviews, which helped us enhance the quality and clarity of this work.

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Authors and Affiliations

  1. Azerbaijan State Oil and Industry University (ASOIU)/French-Azerbaijani University (UFAZ), Baku, Azerbaijan

    Ulviya Abdulkarimova

  2. ICUBE Laboratory, University of Strasbourg, Strasbourg, France

    Ulviya Abdulkarimova

  3. Department of Geophysics, Faculty of Physics and Mathematics, Universidad de Concepción, Concepción, Chile

    Rodrigo Abarca-del-Rio

  4. ITISB, Faculdad de Ingeniera, Universidad Andrés Bello, Viña del Mar, Chile

    Pierre Collet

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  1. Ulviya Abdulkarimova
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Contributions

Conceptualization, R.A.-d.-R. and U. A.; methodology, R.A.-d.-R, U.A., P.C.; software, U.A. and P.C.; validation, R.A.-d.-R and U.A.; formal analysis, U.A. and R.A.-d.-R.; investigation, U.A. and R.A.-d.-R.; data curation, U.A. and R.A.-d.-R.; visualization, U.A., R.A.-d.-R.; supervision, R.A.-d.-R.; project administration, R.A.-d.-R.; U.A.; writing- original draft: U.A. and R.A.-d.-R.; writing-review and editing, U.A., R.A.-d.-R. and P.C.

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Correspondence to Rodrigo Abarca-del-Rio.

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Abdulkarimova, U., Abarca-del-Rio, R. & Collet, P. Harnessing evolutionary algorithms for enhanced characterization of ENSO events. Genet Program Evolvable Mach 26, 4 (2025). https://doi.org/10.1007/s10710-024-09497-z

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