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.
Highlights
A stream-gaging network in a watershed provides the necessary data for withdrawal uses, hydropower production, flood forecast and risk assessment, and hydrological and water quality modeling [1,2]
The momentum constant (0.5) is less than the learning rate (0.75), implying that previous weights have more influence in updating the weights in the Artificial Neural Network (ANN) model compared to new weights
This study compared the performance of Soil and Water Assessment Tool (SWAT) and two machine learning models
Summary
A stream-gaging network in a watershed provides the necessary data for withdrawal uses, hydropower production, flood forecast and risk assessment, and hydrological and water quality modeling [1,2]. Wallis et al [4] found that at least 5% of streamflow records were missing from 1009 United. States Geological Survey stream-gauges for the period from 1948 to 1988 [4]. These data would result in an incorrect response of hydrological models, but it is illogical to ignore abnormal or missing values if there is limited data available; substantial uncertainty in hydrologic and water quality modeling can be driven by these missing records. One drawback of statistical methods is the assumption of linearity between predictors and streamflow [10], resulting in a simplification of streamflow variation and
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