Modeling of migratory beekeeper behaviors with machine learning approach using meteorological and environmental variables: The case of Turkey

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dc.authoridALBAYRAK, AHMET/0000-0002-2166-1102
dc.contributor.authorAlbayrak, Ahmet
dc.contributor.authorCeven, Suleyman
dc.contributor.authorBayir, Raif
dc.date.accessioned2024-09-29T15:55:17Z
dc.date.available2024-09-29T15:55:17Z
dc.date.issued2021
dc.departmentKarabük Üniversitesien_US
dc.description.abstractIn this study, migratory beekeeping behavior, which is an important form of beekeeping, has been modeled. Modeling was performed in conditions of Turkey. Modeling was made by considering food sources (nectar / pollen) and meteorological variables (temperature, humidity, number of rainy days, number of cloudy days and sunshine duration) for Turkey in which migratory beekeeping carried out in a different form than in developed countries. The main output in migratory beekeeping is honey production. Considering honey production, modeling has been made with the food sources and meteorological variables that have the greatest effect on honey production. Since the data set developed for modeling consists of relatively few samples, the ensemble learning approach was preferred from the machine learning approaches. Random Forest and Decision Tree algorithms, which are among the ensemble learning techniques, were used. As a result, the migratory beekeeping behavior was correctly classified at a rate of 92%. As a result of classification of Turkey's 81 provinces in five different categories, it was concluded that 33 provinces are suitable for migratory beekeeping at different times of the year. These 33 provinces are regions in the good and very good categories. In the next stage, thematic maps were produced for migratory beekeepers. Maps were produced for each month of the year. Thus, a guidance and information system has been obtained for migratory beekeepers.en_US
dc.identifier.doi10.1016/j.ecoinf.2021.101470
dc.identifier.issn1574-9541
dc.identifier.issn1878-0512
dc.identifier.scopus2-s2.0-85118541902en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.ecoinf.2021.101470
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4568
dc.identifier.volume66en_US
dc.identifier.wosWOS:000718378900001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofEcological Informaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine learningen_US
dc.subjectMigratory beekeepingen_US
dc.subjectEnsemble learningen_US
dc.subjectSustainable beekeepingen_US
dc.subjectPrecision beekeepingen_US
dc.titleModeling of migratory beekeeper behaviors with machine learning approach using meteorological and environmental variables: The case of Turkeyen_US
dc.typeArticleen_US

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