Potential of kernel and tree-based machine-learning models for estimating missing data of rainfall

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dc.authoridSattari, Mohammad Taghi/0000-0002-5139-2118
dc.authoridS. Band, Shahab/0000-0001-6109-1311
dc.authoridNoman Qasem, Sultan/0000-0002-6575-161X
dc.authoridIRVEM, AHMET/0000-0002-3838-1924
dc.contributor.authorSattari, Mohammad Taghi
dc.contributor.authorFalsafian, Kambiz
dc.contributor.authorIrvem, Ahmet
dc.contributor.authorShahab, S.
dc.contributor.authorQasem, Sultan Noman
dc.date.accessioned2024-09-18T20:32:53Z
dc.date.available2024-09-18T20:32:53Z
dc.date.issued2020
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractIn this study, two kernel-based models were used which include Support Vector Regression (SVR) and Gaussian Process Regression (GPR) and were compared with two tree-based models that are M5 and Random Forest (RF) for estimating missing monthly precipitation data in Antakya, Dortyol, Iskenderun and Samandag stations, which are the important precipitation stations in the Eastern Mediterranean region, Turkey. For this purpose, firstly 10% random precipitation data were assumed as missing data for the period 1980-2019. Secondly, the missing data in each station was estimated with the data of other stations within the framework of four data combinations scenarios. In Kernel-based SVR and GPR methods, the RBF kernel gave suitable results for the selected study area. While SVR and RF methods gave very close estimation results, the SVR method gave relatively better results than the other methods especially in error minimizing aspects. Gaussian function based GPR model generally tries to estimate missing data closer to means. This is the main disadvantage of the GPR model and therefore it is unsuccessful in the estimation process. Finally, the results showed that the algorithms based on machine learning are successful in estimating the missing precipitation data.en_US
dc.identifier.doi10.1080/19942060.2020.1803971
dc.identifier.endpage1094en_US
dc.identifier.issn1994-2060
dc.identifier.issn1997-003X
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85089991952en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1078en_US
dc.identifier.urihttps://doi.org/10.1080/19942060.2020.1803971
dc.identifier.urihttps://hdl.handle.net/20.500.12483/11195
dc.identifier.volume14en_US
dc.identifier.wosWOS:000563072400001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherHong Kong Polytechnic Univ, Dept Civil & Structural Engen_US
dc.relation.ispartofEngineering Applications of Computational Fluid Mechanicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMissing dataen_US
dc.subjectrainfallen_US
dc.subjectmachine learningen_US
dc.subjectrandom Foresten_US
dc.subjectEastern Mediterraneanen_US
dc.subjectTurkeyen_US
dc.titlePotential of kernel and tree-based machine-learning models for estimating missing data of rainfallen_US
dc.typeArticleen_US

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