Machine Learning-Based Approaches and Comparisons for Estimating Missing Meteorological Data and Determining the Optimum Data Set in Nuclear Energy Applications

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dc.contributor.authorTopaloglu, Fatih
dc.date.accessioned2026-06-19T06:39:23Z
dc.date.available2026-06-19T06:39:23Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractGood data analysis is required for the optimal design of nuclear energy projects. However, due to financial or technical reasons, data cannot be collected regularly, which leads to missing data problems. Missing values in data sets can seriously affect research results. There are two main motivations for the study. The first motivation of the study was to define the estimation of missing data in the meteorological data set and its usability in the nuclear energy industry by using Machine Learning (ML)-based Linear Regression (LR), Decision Trees (DT) and Random Forest (RF) algorithms. Its second motivation is to determine the optimum set/number of meteorological data required for nuclear energy projects using the best-performing ML algorithm. For this purpose, 31 years of meteorological data regarding the wind speed, rainfall amount, snowpack and air temperature required for nuclear energy projects by the nuclear policy board in Turkey were analyzed. In this way, some difficulties such as processing and organizing the data created by unnecessary and large data due to its volume and speed have been prevented. In this study, which is based on incomplete meteorological measurement data, the mechanism belongs to the MCAR type. Linear Regression method reached the highest performance with 91.6%. Additionally, by normalizing the data set using Standardization and Normalization scaling techniques, this performance increased to 93.3% and 98.9%, respectively. On the other hand, it has been observed that a 14-year training set is sufficient as a data set in nuclear energy applications.
dc.identifier.doi10.1109/ACCESS.2025.3545361
dc.identifier.endpage37034
dc.identifier.issn2169-3536
dc.identifier.orcid0000-0002-2089-5214
dc.identifier.scopus2-s2.0-86000773549
dc.identifier.scopusqualityQ1
dc.identifier.startpage37019
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2025.3545361
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5587
dc.identifier.volume13
dc.identifier.wosWOS:001494114100009
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorTopaloglu, Fatih
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectNuclear Energy
dc.subjectTemperature Measurement
dc.subjectData Models
dc.subjectTime Series Analysis
dc.subjectRadio Frequency
dc.subjectWind Speed
dc.subjectTemperature Distribution
dc.subjectAccuracy
dc.subjectRandom Forests
dc.subjectLinear Regression
dc.subjectNuclear Energy
dc.subjectMissing Data
dc.subjectMachine Learning
dc.subjectLinear Regression
dc.subjectDecision Trees
dc.subjectRandom Forest
dc.titleMachine Learning-Based Approaches and Comparisons for Estimating Missing Meteorological Data and Determining the Optimum Data Set in Nuclear Energy Applications
dc.typeArticle

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