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http://dx.doi.org/10.21729/ksds.2020.13.4.75

Development of a Data-Driven Model for Forecasting Outflow to Establish a Reasonable River Water Management System  

Yoo, Hyung Ju (Dept. of Civil Engineering, Hongik University)
Lee, Seung Oh (Dept. of Civil Engineering, Hongik University)
Choi, Seo Hye (Korea Institute of Civil Engineering and Building Technology)
Park, Moon Hyung (Korea Institute of Civil Engineering and Building Technology)
Publication Information
Journal of Korean Society of Disaster and Security / v.13, no.4, 2020 , pp. 75-92 More about this Journal
Abstract
In most cases of the water balance analysis, the return flow ratio for each water supply was uniformly determined and applied, so it has been contained a problem that the volume of available water would be incorrectly calculated. Therefore, sewage and wastewater among the return water were focused in this study and the data-driven model was developed to forecast the outflow from the sewage treatment plant. The forecasting results of LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units), and SVR (Support Vector Regression) models, which are mainly used for forecasting the time series data in most fields, were compared with the observed data to determine the optimal model parameters for forecasting outflow. As a result of applying the model, the root mean square error (RMSE) of the GRU model was smaller than those of the LSTM and SVR models, and the Nash-Sutcliffe coefficient (NSE) was higher than those of others. Thus, it was judged that the GRU model could be the optimal model for forecasting the outflow in sewage treatment plants. However, the forecasting outflow tends to be underestimated and overestimated in extreme sections. Therefore, the additional data for extreme events and reducing the minimum time unit of input data were necessary to enhance the accuracy of forecasting. If the water use of the target site was reviewed and the additional parameters that could reflect seasonal effects were considered, more accurate outflow could be forecasted to be ready for climate variability in near future. And it is expected to use as fundamental resources for establishing a reasonable river water management system based on the forecasting results.
Keywords
Return water; River water management system; LSTM; GRU; SVR;
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Times Cited By KSCI : 12  (Citation Analysis)
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