• Journal of Environmental Impact Assessment
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• v.25 no.3
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• pp.222-232
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• 2016

Water Footprint is utilized to analyze direct and indirect water consumption for sustainable water resource management. This study aims to understand potential applicability of water footprint concept by analyzing the status of water consumption and related water policies in Seoul. We analyzed a direct gray water footprint and the blue water footprint in Seoul affected by the social and economic characteristics of the consumers in the city. In particular, in order to analyze the blue water footprint represented by both surface and underground water for the provision and consumption of products, we calculated the actual water consumptions of surface and underground water for 25 districts in Seoul. Our analysis in consideration of population and households indicates that Jung-gu has the highest blue water footprint followed by Jongro-gu, Gangnam-gu, Yongsan-gu, and Seocho-gu. Gray water footprint was calculated by estimating the amount of water for purifying wastewater to meet the water quality standard (above BOD 3.5ppm) for each district. As a result, Jung-gu has the highest gray water footprint, followed by Jongro-gu, Gangnam-gu, Yongsan-gu, Seocho-gu, and Youngdeungpo-gu. Our study suggests the potential value of using water footprint concept to complement the current limitations of water use management focusing on water supply control. We expect that our analysis will provide an important basis for considering water use management which is economically and socially more resilient and sustainable.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.75-92
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• 2020

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.