• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.246-246
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• 2023

Multi-purpose dams are operated accounting for both physical and socioeconomic factors. This study aims to evaluate the utility of a deep learning algorithm-based model for three multi-purpose dam operation (Seomjin River dam, Juam dam, and Juam Control dam) in Seomjin River. In this study, the Gated Recurrent Unit (GRU) algorithm is applied to predict hourly water level of the dam reservoirs over 2002-2021. The hyper-parameters are optimized by the Bayesian optimization algorithm to enhance the prediction skill of the GRU model. The GRU models are set by the following cases: single dam input - single dam output (S-S), multi-dam input - single dam output (M-S), and multi-dam input - multi-dam output (M-M). Results show that the S-S cases with the local dam information have the highest accuracy above 0.8 of NSE. Results from the M-S and M-M model cases confirm that upstream dam information can bring important information for downstream dam operation prediction. The S-S models are simulated with altered outflows (-40% to +40%) to generate the simulated water level of the dam reservoir as alternative dam operational scenarios. The alternative S-S model simulations show physically inconsistent results, indicating that our deep learning algorithm-based model is not explainable for multi-purpose dam operation patterns. To better understand this limitation, we further analyze the relationship between observed water level and outflow of each dam. Results show that complexity in outflow-water level relationship causes the limited predictability of the GRU algorithm-based model. This study highlights the importance of socioeconomic factors from hidden multi-purpose dam operation processes on not only physical processes-based modeling but also aritificial intelligence modeling.

• Magazine of the Korean Society of Agricultural Engineers
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• v.37 no.5
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• pp.35-42
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• 1995

his paper is to develop a GIS application model (GISCELWAB) for the spatial simulation of surface runoff from a small watershed. The model was constituted by three submodels : The input data extraction model (GISINDATA) which prepares cell-based input data automatically for a given watershed, the cell water balance model (CELWAB) which calculates the water balance for a cell and simulates surface runoff of watershed simultaneously by the interaction of cells, and the output data management model (GISOUTDISP) which visualize the results of temporal and spatial variation of surface runoff. The input data extraction model was developed to solve the time-consuming problems for the input-data preparation of distributed hydrologic model. The input data for CELWAB can be obtained by extracting ASCII data from a vector map. The output data management model was developed to convert the storage depth and discharge of cells into grid map. This model enables to visualize the spatial formulation process of watershed storage depth and surface runoff wholly with time increment.

• Proceedings of the Korea Port Economic Association Conference
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• 2006.08a
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• pp.103-124
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• 2006

This paper is concerned with a comparative study on the economic effects upon the Korean economy by transport mode, ie road, rail, air, and coastal and inland water transportation between 1990 and 2000, using input-output analysis. The economic effects consist of backward and forward linkage effects, production inducing and import-inducing effects. The data employed for this study come from the Bank of Korea database for Input-Output structure of the Korean economy. The major findings in this study are, among others:(1) the power of dispersion(=backward linkage effect) of coastal and inland water transportation sector is the highest among the four transport sectors, while road cargo transport mode showed the highest the degree of sensitivity (=forward linkage effect); (2) rail cargo transport recorded the highest production inducing effects; and (3) air transport mode achieved the highest ranking in the import-inducing effects.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.265-273
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• 2016

A water wall system is one of the most important components of a boiler in a thermal power plant, and it is a nonlinear Multi-Input and Multi-Output (MIMO) system, with 6 inputs and 3 outputs. Three models are developed and comp for the controller design, including a linear model, a multilayer feed-forward neural network (MFNN) model and an Echo State Network (ESN) model. First, the linear model is developed by linearizing a given nonlinear model and is analyzed as a function of the operating point. Second, the MFNN and the ESN are developed by using training data from the nonlinear model. The three models are validated using Matlab with nonlinear input-output data that was not used during training.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.363-363
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• 2023

Artificial Neural Networks (ANN) is a powerful tool for addressing various practical problems and it has been extensively applied in areas of water resources. In this study, Artificial Neural Networks (ANNs) were developed for flood forecasting at specific locations on the Han River. The Partial Mutual Information (PMI) technique was used to select input variables for ANNs that are neither over-specified nor under-specified while adequately describing the underlying input-output relationships. Historical observations including discharges at the Paldang Dam, flows from tributaries, water levels at the Paldang Bridge, Banpo Bridge, Hangang Bridge, and Junryu gauge station, and time derivatives of the observed water levels were considered as input candidates. Lagged variables from current time t to the previous five hours were assumed to be sufficient in this study. A three-layer neural network with one hidden layer was used and the neural network was optimized by selecting the optimal number of hidden neurons given the selected inputs. Given an ANN architecture, the weights and biases of the network were determined in the model training. The use of PMI-based input variable selection and optimized ANNs for different sites were proven to successfully predict water levels during flood periods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.4
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• pp.887-896
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• 1994

It is common practice in the case of optimum reservoir operation model that the reservoir inflow series are generated by stochastic model with keeping other variable such as water demands from the reservoir constant. However, when the input and output of the water distribution system have close relationship the output variables can be stochastically generated in relation with the input variables. In the present study the reservoir inflow series, the input of the system, is generated by periodic autoregressive model with constant parameter, and the agricultural water demand series, the output, is generated using periodic multivariate autoregressive model with constant parameter. The time period of the data series generated is taken as 10-day which is the common period used for agricultural water uses. The results of data generation by two different models showed that the periodic stochastic models well represent the characteristics of the historical time series, and that in the case of generating model for agricultural demand series it has closer relation with reservoir inflow than with the series itself.

• Journal of Environmental Science International
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• v.26 no.11
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• pp.1255-1266
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• 2017

Phosphorus (P) is an essential and major nutrient for both plants and animals. However, anthropogenic P in the environment may cause severe problems such as the deterioration of water quality. Therefore, it is essential for the Korean government to manage P in the agricultural sector. The annual P budget for Korea was 46 kg P ha-1 in 2013, placing Korea in second among Organisation for Economic Co-operation and Development (OECD) countries. P surplus and deficiency in agricultural lands can be estimated according to the P budget, which is one of the OECD agri-environment indicators. In the P budget, it is important to ensure consistency in the input-output data sources, in order to apply national and regional policies for the environmentally sound management of agricultural P. This study examines the impacts on the input-output data sources in the regional P budget in Korea. P budgets were between 99-145 kg-P/ha, depending on different data sources. We suggest two recommended data combinations (DC 1 and DC 2) for reliability of the data. P budgets calculated using DC 1 and DC 2 were 128 kg-P/ha and 97 kg-P/ha, respectively. According to the results, one of the core factors affecting P budgets was crop production. In this study, DC 2 was recommended rather than DC 1 in order to consider the cultivated areas for various crops. It is also necessary to analyze the sensitivity of the coefficients used in P budget in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.368-368
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• 2022

The Soil and Water Assessment Tool (SWAT) has been widely used to simulate the long-term hydrological conditions of a catchment. Two output variables, outflow and sediment yield have been widely investigated in the field of water resources management, especially in determining the conditions of ungauged subbasins. The presence of missing data in weather input data can cause poor representation of the climate conditions in a catchment especially for large or mountainous catchments. Therefore, in this study, a custom module was developed and evaluated to determine the efficiency of utilizing basic spatial interpolation methods in the estimation of weather input data. The module has been written in Python language and can be considered as a pre-processing module prior to using the SWAT model. The results of this study suggests that the utilization of the proposed pre-processing module can improve the simulation results for both outflow and sediment yield in a catchment, even in the presence of missing data.

• Korean Journal of Organic Agriculture
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• v.9 no.3
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• pp.45-71
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• 2001

The duck-rice forming system is increasingly spread up throughout Korea since 1992. It is discussed the rice field, rice quality, weed and pest management in the duck-rice weeding system compared to conventional farming system. Moreover the optimizing duck population, system management and fertilizer application rate were reported. Energy input and output by duck-rice farming system were carefully compared with those of low input sustainable paddy field and conventional farming paddy field. To find out the environmentally sound function of duck-rice system, the total nitrogen in paddy soil and paddy water, and nitrogen cycle in paddy rice cultivation system were analysed. finally the input and output were calculated, and ecological characteristic were determined in terms of nitrogen balance, labor input, animal input, renewable energy input, turnover of soil organic matter, energy loss, non-renewable indirect and direct energy input. It was concluded duck-rice weeding system could be recommended in terms of net only environmentally sound, but also farmer's income. But there are still some research needs for successful adaption of duck-rice farming to investigate to determine the optimal population of duck in rice paddy field unit, release time of duckling, duck management after release, and strategy for duck marketing and duck processing.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.4
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• pp.859-866
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• 1994

It is difficult to make an exact estimate of the peak discharge or the runoff depth of flood and establish the proper measure for the flood protection since the water stage or discharge has been nearly measured at most medium or small river basins. The objective of this study is to estimate parameters of the discrete, linear, input-output model for medium or small river basin. The On-Cheon River basin in Pusan was selected for the study area. The runoff data used in the study has been observed since June 1993, and the effective rainfall was determined using the storage function method. The parameter sets of the discrete, linear, input-output model were estimated using the least squares method and the correlation function method, respectively. The calculated hydrographs by the discrete, linear, input-output model regenerated the observed outflow hydrographs well, and also the simulated flood hydrograph was comparable to the observed one. Therefore, it is believed that the discrete, linear, input-output model is simpler than other runoff analysis methods, and can be applied to a medium or small river basin.