• Journal of Convergence for Information Technology
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• v.10 no.5
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• pp.117-125
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• 2020

Our study identifies a cost and a benefit incurred in implementing an investment project to offer new public services by use of food waste disposers and direct input of feces in sewers. This is done with identifying costs of each processing division and benefits of the project by objective statistical data and engineering perspective. In summary, cost items identified are as follows: there are house laterals, removal of septic tanks, etc. for sewer pipes system. As to water quality conservation, cost incurs in storm water outfalls and divert chambers, sewage storage tanks, equipment to treat sewer overflows, and so on. With respect to sewage treatment plants(STPs), there are so many items as increase of contaminant loads in influent of STPs, and other items. There are benefit items in health improvement due to odor mitigation, increase of energy productivity, saving cost of food waste treatment and cleaning septic tanks, etc. These estimates will be used as a basic data for its economic effect.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.301-309
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• 2021

In this study, we tried to improve the performance of the existing U-net-based deep learning rainfall prediction model, which can weaken the meaning of time series order. For this, ConvLSTM2D U-Net structure model considering temporal consistency of data was applied, and we evaluated accuracy of the ConvLSTM2D U-Net model using a RainNet model and an extrapolation-based advection model. In addition, we tried to improve the uncertainty in the model training process by performing learning not only with a single model but also with 10 ensemble models. The trained neural network rainfall prediction model was optimized to generate 10-minute advance prediction data using four consecutive data of the past 30 minutes from the present. The results of deep learning rainfall prediction models are difficult to identify schematically distinct differences, but with ConvLSTM2D U-Net, the magnitude of the prediction error is the smallest and the location of rainfall is relatively accurate. In particular, the ensemble ConvLSTM2D U-Net showed high CSI, low MAE, and a narrow error range, and predicted rainfall more accurately and stable prediction performance than other models. However, the prediction performance for a specific point was very low compared to the prediction performance for the entire area, and the deep learning rainfall prediction model also had limitations. Through this study, it was confirmed that the ConvLSTM2D U-Net neural network structure to account for the change of time could increase the prediction accuracy, but there is still a limitation of the convolution deep neural network model due to spatial smoothing in the strong rainfall region or detailed rainfall prediction.

• Korean Journal of Soil Science and Fertilizer
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• v.41 no.2
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• pp.143-152
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• 2008

In order to share the understanding agricultural multifunctionality with people, we carried out quantification and monetary evaluation for controlling flood and fostering water resources function in paddy and upland farming in Korea. The amount of water controlling flood and fostering water resources function in paddy farming was much greater than that in upland. The quantification of flood control function could be estimated by sum of the height of dike and water infiltrated during the flooding periods in paddy farming, and water excepting runoff water from precipitation at flooding time in upland farming. As results of estimation of flood control function, the amounts of water controlling flood have been evaluated as $294mm\;year^{-1}$ in paddy farming and $72.6mm\;year^{-1}$ upland farming, and was calculated 3.71 billion MT on a nation basis in 2006. When it was carried out monetary estimation as the cost of dam construction and the depreciation expense by using replacement cost method, flood control functions in paddy and upland were evaluated as 44,338.9 and 7,221.5 billion won, respectively. Comparing with previous reports, monetary value was analyzed much to increase because of rising price cost recently. Fostering water resource functions were also quantified in paddy and upland farming as the amount of water keeping and infiltrating water during the cultivation. In the basis of estimation model, it was showed that paddy and upland farming had been estimated to have $414.28mm\;year^{-1}$, $18.7mm\;year^{-1}$, respectively. They were also calculated to 4.49 and 0.137 billion MT on a nation basis in 2006, respectively. The economic values of fostering water resources function in paddy and upland farming were also estimated to 1,769.4 and 52.8 billion won, respectively, as replacing the amount of water to the cost of drinking water in 2006. There were differences by much to the amounts of controlling flood function and fostering water resource between paddy and upland farming. It means that paddy farming more play an important role in environment than upland farming in Korea.