Predictive Model of the ENSO Phenomenon Based on Regression Trees

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dc.contributor.authorMendoza Uribe, Indalecio
dc.coverage.issue1cs
dc.coverage.volume29cs
dc.date.accessioned2024-01-11T08:34:35Z
dc.date.available2024-01-11T08:34:35Z
dc.date.issued2023-06-30cs
dc.description.abstractIn this work, the supervised machine learning technique was applied to develop a predictive model of the phase of the El NiƱo-Southern Oscillation (ENSO) phenomenon. Regression trees were specifically used by means of the Scikit-Learn library of the Python programming language. Data from the period 1950-2022 were used as training and test. The performance of the predictive model was validated using three continuous type error measurement metrics: Mean Absolute Error, Maximum Error and Root Mean Square Root. The results indicate that with a greater number of training data the model improves its performance, with a tendency to decrease the error in forecasts. Which starts for the year 1953 with errors of 0.77, 1.41 and 0.75 for MAE, ME and RMSE respectively, ending for the year 2022 with errors of 0.28, 0.72 and 0.13 for the same metrics. It is concluded that, based on the results, the developed model is consistent and reliable for ENSO phase forecasts in a 12-month window.en
dc.formattextcs
dc.format.extent7-14cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMendel. 2023 vol. 29, č. 1, s. 7-14. ISSN 1803-3814cs
dc.identifier.doi10.13164/mendel.2023.1.007en
dc.identifier.issn2571-3701
dc.identifier.issn1803-3814
dc.identifier.urihttps://hdl.handle.net/11012/244236
dc.language.isoencs
dc.publisherInstitute of Automation and Computer Science, Brno University of Technologycs
dc.relation.ispartofMendelcs
dc.relation.urihttps://mendel-journal.org/index.php/mendel/article/view/210cs
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0en
dc.subjectclimatic variationen
dc.subjectdeterministic forecasten
dc.subjectmachine learningen
dc.subjectsupervised classificationen
dc.subjectverification methodsen
dc.titlePredictive Model of the ENSO Phenomenon Based on Regression Treesen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta strojnĆ­ho inženĆ½rstvĆ­cs
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