SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Comparative studies of different imputation methods for recovering streamflow observation 
Authors:Kim, M., S. Baek, M. Ligaray, J. Pyo, M. Park and K.H. Cho 
Volume (Issue):7(12) 
Article ID: 
URL (non-DOI journals): 
Broad Application Category:hydrologic only 
Primary Application Category:model and/or data comparison 
Secondary Application Category:hydrologic assessment 
Watershed Description:643.96 km^2 Taehwa River, located in southeast South Korea 
Calibration Summary: 
Validation Summary: 
General Comments: 
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. 
Keywords:data imputation; streamflow; soil and water assessment tool (SWAT); artificial neuralnetwork (ANN); self organizing map (SOM)