Prediction of global temperature anomaly by machine learning based techniques

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dc.authoridSen, Doruk/0000-0003-3353-5952|Gunay, M. Erdem/0000-0003-1282-718X
dc.authorwosidSen, Doruk/D-4547-2016
dc.contributor.authorSen, Doruk
dc.contributor.authorHuseyinoglu, Mehmet Fatih
dc.contributor.authorGunay, M. Erdem
dc.date.accessioned2024-07-18T20:40:37Z
dc.date.available2024-07-18T20:40:37Z
dc.date.issued2023
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractIn this work, anthropogenic and natural factors were used to evaluate and forecast climate change on a global scale by using a variety of machine-learning techniques. First, significance analysis using the Shapley method was conducted to compare the importance of each variable. Accordingly, it was determined that the equivalent CO2 concentration in the atmosphere was the most important variable, which was proposed as further evidence of climate change due to fossil fuel-based energy generation. Following that, a variety of machine learning approaches were utilized to simulate and forecast the temperature anomaly until 2100 based on six distinct scenarios. Compared to the preindustrial period, the temperature anomaly for the best-case scenario was found to increase a mean value of 1.23 degrees C and 1.11 degrees C for the mid and end of the century respectively. On the other hand, the anomaly was estimated for the worst-case scenario to reach to a mean value of 2.52 degrees C and 4.97 degrees C for the same periods. It was then concluded that machine learning approaches can assist researchers in predicting climate change and developing policies for national governments, such as committing firmly to renewable energy regulations.en_US
dc.identifier.doi10.1007/s00521-023-08580-3
dc.identifier.endpage15614en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue21en_US
dc.identifier.scopus2-s2.0-85152521984en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage15601en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-023-08580-3
dc.identifier.urihttps://hdl.handle.net/11411/7141
dc.identifier.volume35en_US
dc.identifier.wosWOS:000969749800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectTemperature Anomalyen_US
dc.subjectGlobal Warmingen_US
dc.subjectSolar Variablesen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectSupport Vector Regressionen_US
dc.subjectDeep Learningen_US
dc.subjectSocioeconomic Indicatorsen_US
dc.subjectClimate-Changeen_US
dc.subjectConsumptionen_US
dc.subjectNetworken_US
dc.titlePrediction of global temperature anomaly by machine learning based techniques
dc.typeArticle

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