Drought analysis using innovative trend analysis and machine learning models for Eastern Black Sea Basin

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dc.authoridNiazkar, Majid/0000-0002-5022-1026
dc.authoridPiraei, Reza/0000-0001-7129-8076
dc.authoridHIRCA, Tugce/0000-0002-5694-767X
dc.authoridGangi, Fabiola/0000-0002-9192-4369
dc.contributor.authorNiazkar, Majid
dc.contributor.authorPiraei, Reza
dc.contributor.authorTurkkan, Gokcen Eryilmaz
dc.contributor.authorHirca, Tugce
dc.contributor.authorGangi, Fabiola
dc.contributor.authorAfzali, Seied Hosein
dc.date.accessioned2024-10-04T18:49:32Z
dc.date.available2024-10-04T18:49:32Z
dc.date.issued2024
dc.departmentBayburt Üniversitesien_US
dc.description.abstractThis study aims to assess the Eastern Black Sea Basin drought conditions. For this purpose, the trend changes in SPI values of 6, 9, 12, and 24 months using innovative trend analysis were examined. Additionally, four machine learning models, including Multiple Linear Regression, Artificial Neural Networks, K Nearest Neighbors, and XGBoost Regressor, are employed to forecast SPI with rainfall data between 1965 and 2020 from eight rainfall stations. The input data for each model was SPI values from lead times of 1 to 6, resulting into 768 unique scenarios. The ML models estimated SPI values better as the SPI duration increased, with the 24-month SPI showing the highest accuracy. The results of SPI forecast indicated that the optimal model and number of input variables varied for each SPI and station, indicating that further studies are required to improve SPI predictions.en_US
dc.identifier.doi10.1007/s00704-023-04710-y
dc.identifier.endpage1624en_US
dc.identifier.issn0177-798X
dc.identifier.issn1434-4483
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85175297118en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1605en_US
dc.identifier.urihttps://doi.org/10.1007/s00704-023-04710-y
dc.identifier.urihttp://hdl.handle.net/20.500.12403/3200
dc.identifier.volume155en_US
dc.identifier.wosWOS:001091300000001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Wienen_US
dc.relation.ispartofTheoretical and Applied Climatologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectStandardized Precipitation Indexen_US
dc.subjectRegressionen_US
dc.subjectNetworken_US
dc.titleDrought analysis using innovative trend analysis and machine learning models for Eastern Black Sea Basinen_US
dc.typeArticleen_US

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