Assessment of Different Methods for Estimation of Missing Rainfall Data

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dc.authoridHIRCA, Tugce/0000-0002-5694-767X
dc.contributor.authorHirca, Tugce
dc.contributor.authorTurkkan, Goekcen Eryilmaz
dc.date.accessioned2024-10-04T18:48:19Z
dc.date.available2024-10-04T18:48:19Z
dc.date.issued2024
dc.departmentBayburt Üniversitesien_US
dc.description.abstractMissing data is a common problem encountered in various fields, including clinical research, environmental sciences and hydrology. In order to obtain reliable results from the analysis, the data inventory must be completed. This paper presents a methodology for addressing the missing data problem by examining the missing data structure and missing data techniques. Simulated datasets were created by considering the number of missing data, missing data pattern and missing data mechanism of real datasets containing missing values, which are often overlooked in hydrology. Considering the missing data pattern, the most commonly used methods for missing data analysis in hydrology and other fields were applied to the created simulated datasets. Simple imputation techniques and expectation maximization (EM) were implemented in SPSS software and machine learning techniques such as k-nearest neighbor (kNN), together with the hot-deck were implemented in the Python programming language. In the performance evaluation based on error metrics, it is concluded that the EM method is the most suitable completion method. Homogeneity analyses were performed in the Mathematica programming language to identify possible changes and inconsistencies in the completed rainfall dataset. Homogeneity analyses revealed that most of the completed rainfall datasets are homogeneous at class 1 level, consistent and reliable and do not show systematic changes in time.en_US
dc.description.sponsorshipBayburt Universityen_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.1007/s11269-024-03936-3
dc.identifier.issn0920-4741
dc.identifier.issn1573-1650
dc.identifier.scopus2-s2.0-85200113885en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s11269-024-03936-3
dc.identifier.urihttp://hdl.handle.net/20.500.12403/3018
dc.identifier.wosWOS:001281327700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofWater Resources Managementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSusurluk basinen_US
dc.subjectMissing rainfall dataen_US
dc.subjectMissing data patternen_US
dc.subjectMissing data mechanismen_US
dc.subjectExpectation-maximizationen_US
dc.titleAssessment of Different Methods for Estimation of Missing Rainfall Dataen_US
dc.typeArticleen_US

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