Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation

Water

Minjeong Kim²  Sangsoo Baek²  Mayzonee Ligaray²  Jongcheol Pyo²  Minji Park¹  Kyung Hwa Cho² 

[1] Han-River Environmental Research Center, Gyeonggi-do 476-823, Korea;School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Korea;

关键词: data imputation;    streamflow;    soil and water assessment tool (SWAT);    artificial neural network (ANN);    self organizing map (SOM);   

DOI  :  10.3390/w7126663

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.