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Water 2015, 7(12), 6847-6860; doi:10.3390/w7126663

Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation

1
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Korea
2
Han-River Environmental Research Center, Gyeonggi-do 476-823, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Miklas Scholz
Received: 1 October 2015 / Revised: 23 November 2015 / Accepted: 27 November 2015 / Published: 4 December 2015
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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. View Full-Text
Keywords: data imputation; streamflow; soil and water assessment tool (SWAT); artificial neural network (ANN); self organizing map (SOM) data imputation; streamflow; soil and water assessment tool (SWAT); artificial neural network (ANN); self organizing map (SOM)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kim, M.; Baek, S.; Ligaray, M.; Pyo, J.; Park, M.; Cho, K.H. Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation. Water 2015, 7, 6847-6860.

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