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• W1913009830 abstract "Precipitation is the main factor that drives hydrologic modeling; therefore, missing precipitation data can cause malfunctions in hydrologic modeling. Although interpolation of missing precipitation data is recognized as an important research topic, only a few methods follow a regression approach. In this study, daily precipitation data were interpolated using five different kernel functions, namely, Epanechnikov, Quartic, Triweight, Tricube, and Cosine, to estimate missing precipitation data. This study also presents an assessment that compares estimation of missing precipitation data through<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M1><mml:mrow><mml:mi>K</mml:mi></mml:mrow></mml:math>th nearest neighborhood (<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M2><mml:mrow><mml:mi>K</mml:mi></mml:mrow></mml:math>NN) regression to the five different kernel estimations and their performance in simulating streamflow using the Soil Water Assessment Tool (SWAT) hydrologic model. The results show that the kernel approaches provide higher quality interpolation of precipitation data compared with the<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M3><mml:mrow><mml:mi>K</mml:mi></mml:mrow></mml:math>NN regression approach, in terms of both statistical data assessment and hydrologic modeling performance." @default.
• W1913009830 created "2016-06-24" @default.
• W1913009830 creator A5011224257 @default.
• W1913009830 creator A5078843299 @default.
• W1913009830 date "2015-01-01" @default.
• W1913009830 modified "2023-09-27" @default.
• W1913009830 title "Interpolation of Missing Precipitation Data Using Kernel Estimations for Hydrologic Modeling" @default.
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• W1913009830 doi "https://doi.org/10.1155/2015/935868" @default.
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