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Frequency based imputation of precipitation

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dc.contributor.author Dikbas, F.
dc.date.accessioned 2019-08-16T12:57:01Z
dc.date.available 2019-08-16T12:57:01Z
dc.date.issued 2017
dc.identifier.issn 14363240 (ISSN)
dc.identifier.uri http://acikerisim.pau.edu.tr:8080/xmlui/handle/11499/8865
dc.description.abstract Changing climate and precipitation patterns make the estimation of precipitation, which exhibits two-dimensional and sometimes chaotic behavior, more challenging. In recent decades, numerous data-driven methods have been developed and applied to estimate precipitation; however, these methods suffer from the use of one-dimensional approaches, lack generality, require the use of neighboring stations and have low sensitivity. This paper aims to implement the first generally applicable, highly sensitive two-dimensional data-driven model of precipitation. This model, named frequency based imputation (FBI), relies on non-continuous monthly precipitation time series data. It requires no determination of input parameters and no data preprocessing, and it provides multiple estimations (from the most to the least probable) of each missing data unit utilizing the series itself. A total of 34,330 monthly total precipitation observations from 70 stations in 21 basins within Turkey were used to assess the success of the method by removing and estimating observation series in annual increments. Comparisons with the expectation maximization and multiple linear regression models illustrate that the FBI method is superior in its estimation of monthly precipitation. This paper also provides a link to the software code for the FBI method. © 2016, Springer-Verlag Berlin Heidelberg.
dc.language.iso English
dc.publisher Springer New York LLC
dc.relation.isversionof 10.1007/s00477-016-1356-x
dc.subject Data-driven modelling
dc.subject Estimation of missing data
dc.subject Frequency based imputation
dc.subject Precipitation
dc.subject Estimation
dc.subject Linear regression
dc.subject Maximum principle
dc.subject Precipitation (chemical)
dc.subject Regression analysis
dc.subject Data driven modelling
dc.subject Expectation - maximizations
dc.subject Missing data
dc.subject Multiple linear regression models
dc.subject One-dimensional approach
dc.subject Precipitation patterns
dc.subject Precipitation time series
dc.subject Frequency estimation
dc.subject climate change
dc.subject frequency analysis
dc.subject modeling
dc.subject one-dimensional modeling
dc.subject precipitation (climatology)
dc.subject precipitation assessment
dc.subject software
dc.subject time series
dc.subject Turkey
dc.title Frequency based imputation of precipitation
dc.type Article
dc.relation.journal Stochastic Environmental Research and Risk Assessment
dc.identifier.volume 31
dc.identifier.issue 9
dc.identifier.startpage 2415
dc.identifier.endpage 2434
dc.identifier.index Scopus

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