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Cho, Kyung Hwa
Water-Environmental Informatics Lab (WEIL)
Research Interests
  • Water Quality Monitoring and Modeling, Water Treatment Process Modeling

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Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation

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Title
Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation
Author
Kim, MinjeongBaek, SangsooLigaray, MayzoneePyo, JongcheolPark, MnjiCho, Kyung Hwa
Issue Date
2015-12
Publisher
MDPI AG
Citation
WATER, v.7, no.12, pp.6847 - 6860
Abstract
Faulty field sensors cause unreliability in the observed data that needed to calibrate and assess hydrology models. However, it is illogical to ignore abnormal or missing values if there are limited data available. This study addressed this problem by applying data imputation to replace incorrect values and recover missing streamflow information in the dataset of the Samho gauging station at Taehwa River (TR), Korea from 2004 to 2006. Soil and Water Assessment Tool (SWAT) and two machine learning techniques, Artificial Neural Network (ANN) and Self Organizing Map (SOM), were employed to estimate streamflow using reasonable flow datasets of Samho station from 2004 to 2009. The machine learning models were generally better at capturing high flows, while SWAT was better at simulating low flows.
URI
https://scholarworks.unist.ac.kr/handle/201301/17970
URL
http://www.mdpi.com/2073-4441/7/12/6663
DOI
10.3390/w7126663
ISSN
2073-4441
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UEE_Journal Papers
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