• Journal of Korea Water Resources Association
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• v.45 no.5
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• pp.481-493
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• 2012

The Uncertainty of drought events can be regarded as supernatural phenomena so that the uncertainty of water supply system will be also uncontrollable. Decision making for water supply system operation must be dealt with in consideration of hydrologic uncertainty conditions. When ultimate small quantity of precipitation or streamflow lasts, water supply system might be impacted as well as stream pollution, aqua- ecosystem degradation, reservoir dry-up and river aesthetic waste etc. In case of being incapable of supplying water owing to continuation of severe drought, it can make the damage very serious beyond our prediction. This study analyzes comprehensively sustained drought impacts on the Han River Basin Water Supply System. Drought scenarios consisted of several sustained times and return periods for 5 sub-watersheds are generated using a stochastic hydrologic time series model. The developed drought scenarios are applied to assess water supply performance at the Paldang Dam. The results show that multi-year drought events reflecting spatial hydrologic diversity need to be examined in order to recognize variation of the unexpected drought impacts.

• Journal of Korea Water Resources Association
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• v.41 no.9
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• pp.895-905
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• 2008

To analyze hydrologic processes in a watershed requires both various geographical data and hydrological time series data. Recently, not only geographical data such as DEM(Digital Elevation Model) and hydrologic thematic map but also hydrological time series from numerical weather prediction and rainfall radar have been provided as grid data, and there are studies on hydrologic analysis using these grid data. In this study, GRM(Grid based Rainfall-runoff Model) which is physically-based distributed rainfall-runoff model has been developed to simulate short term rainfall-runoff process effectively using these grid data. Kinematic wave equation is used to simulate overland flow and channel flow, and Green-Ampt model is used to simulate infiltration process. Governing equation is discretized by finite volume method. TDMA(TriDiagonal Matrix Algorithm) is applied to solve systems of linear equations, and Newton-Raphson iteration method is applied to solve non-linear term. Developed model was applied to simplified hypothetical watersheds to examine model reasonability with the results from $Vflo^{TM}$. It was applied to Wicheon watershed for verification, and the applicability to real site was examined, and simulation results showed good agreement with measured hydrographs.

• Journal of Korea Water Resources Association
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• v.51 no.6
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• pp.503-514
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• 2018

In recent, the hydrological regime of the Mekong river is changing drastically due to climate change and haphazard watershed development including dam construction. Information of hydrologic feature like streamflow of the Mekong river are required for water disaster prevention and sustainable water resources development in the river sharing countries. In this study, runoff simulations at the Kratie station of the lower Mekong river are performed using SWAT (Soil and Water Assessment Tool), a physics-based hydrologic model, and LSTM (Long Short-Term Memory), a data-driven deep learning algorithm. The SWAT model was set up based on globally-available database (topography: HydroSHED, landuse: GLCF-MODIS, soil: FAO-Soil map, rainfall: APHRODITE, etc) and then simulated daily discharge from 2003 to 2007. The LSTM was built using deep learning open-source library TensorFlow and the deep-layer neural networks of the LSTM were trained based merely on daily water level data of 10 upper stations of the Kratie during two periods: 2000~2002 and 2008~2014. Then, LSTM simulated daily discharge for 2003~2007 as in SWAT model. The simulation results show that Nash-Sutcliffe Efficiency (NSE) of each model were calculated at 0.9(SWAT) and 0.99(LSTM), respectively. In order to simply simulate hydrological time series of ungauged large watersheds, data-driven model like the LSTM method is more applicable than the physics-based hydrological model having complexity due to various database pressure because it is able to memorize the preceding time series sequences and reflect them to prediction.

• Journal of Korea Water Resources Association
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• v.49 no.1
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• pp.1-9
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• 2016

A correlation analysis between raw water turbidity at two wide-area water treatment plants and hydrological data was conducted for efficient water supply, design and management of water treatment plant. Both correlation analysis and principal component analysis were conducted using hydrological time series data such as inflow discharge, outflow discharge, and rainfall at dam basin of intake station of wide-area water treatment plants. And, forecasting of change in turbidity was conducted using regression equation for turbidity prediction. The raw water turbidity of two water treatment plants was strongly related to time series of discharge. The raw water turbidity of Chungju water treatment plant is strongly related to outflow discharge at Chungju dam (0.708). Whereas, the raw water turbidity of Wabu water treatment plant is strongly related to inflow discharge at Paldang dam (0.805). Similar trends between turbidity forecasting result using regression equation and calculation result using estimation equation on Korea water supply facilities standard were obtained. The result of this study can provide basic data for construction and management of water treatment plant.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.34 no.6
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• pp.617-623
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• 2001

To predict the seasonal variation of dissolved oxygen (DO) in Hakata bay, Japan, possible 20 time-series of different hourly-solar-radiation intensities were generated based on stochastically changing solar radiation intensity, and a numerical simulation on dissolved oxygen (DO) was carried out for each time series by using the Sediment-Water Ecological Model (SWEM). The model, consisting of two sub-models with hydrodynamic and biological models, simulates the circulation process of nutrient between water column and sediment, such as nutrient regeneration from sediments as well as ecological structures on the growth of phytoplankton and zooplankton, The results of the model calibration followed the seasonal variation of observed water quality well, and generated cumulative-frequency-distribution (CFD) curves of daily solar radiation agreed well with observed ones, The simulation results indicated that the exchange of sea water would have a great influence on the DO concentration, and that the concentration could change more than 1 mg/L in a day. This prediction method seems to be an effective way to examine a solution to minimize fishery damage when DO is depleted.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.5
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• pp.505-513
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• 2010

This study investigate the characteristics of tidal constituents, and long-term mean sea level oscillations at Incheon bay. For this, the conditions of three tide stations around Incheon bay have examined, and carried out harmonic analysis on water level data for periods of about 40 years(1960~2007). Four major tidal constituents($M_2$, $S_2$, $K_1$, $O_1$) of each tide station showed tendency that change over the 18.61year lunar node cycle, and the type of tide at three stations is mainly semi-diurnal tides. And also, the past monthly tidal modulations are especially sensitive to the cumulative year of water level data in accuracy of tidal prediction. In case that regard the detached data at three tide stations as a single time series data of 40 years, the results of analysis on a single time series, long-term mean sea level oscillations and modulations of tidal datum at tide stations appears with a range of about 10cm, respectively. In addition, the predicted tides at the Inchcon harbor by global and regional tide models of OSU(Oregon State University) based on various satellite altimetric(Topex Poseidon, Topex Tandem, ERS, GFO) data are compared with the observed tides by KHOA(the Korea Hydrographic and Oceanographic Administration). The results show that the high resolution regional model is a quite good agreement at coastal shallow water region.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.71-85
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• 2021

A variety of methods have been proposed through a number of studies on sophisticated demand forecasting models that can reduce logistics costs. These studies mainly determine the applicable demand forecasting model based on the pattern of demand quantity and try to judge the accuracy of the model through statistical verification. Demand patterns can be broadly divided into regularity and irregularity. A regular pattern means that the order is regular and the order quantity is constant. In this case, predicting demand mainly through regression model or time series model was used. However, this demand is called "intermittent demand" when irregular and fluctuating amount of order quantity is large, and there is a high possibility of error in demand prediction with existing regression model or time series model. For items that show intermittent demand, predicting demand is mainly done using Croston or HOLTS. In this study, we analyze the demand patterns of various items of air cargo with intermittent patterns and apply the most appropriate model to predict and verify the demand. In this process, intermittent optimal demand forecasting model of air cargo is proposed by analyzing the fit of various models of air cargo by item and region.

• Journal of The Geomorphological Association of Korea
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• v.18 no.1
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• pp.41-56
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• 2011

This study analyzes the change in land-cover based on satellite imagery to draw up land-cover map in the future, and estimates the change in land category using statistical data of the land category. To estimate land category, this study applied the double exponentially smoothing method. The result of the land cover classification according to year using satellite imagery showed that the type with the largest increase in area of land cover change in the cities of Cheonan and Asan was artificial structure, followed by water, grass field and bare land. However forest, paddy, marsh and dry field were reduced. Further, the result of the time-series analysis of the land category was found to be similar to the result of the land cover classification using satellite imagery. Especially, the result of the estimation of the land category change using the double exponentially smoothing method showed that paddy, dry field, forest and marsh are anticipated to consistently decrease in area from 2010 to 2100, whereas artificial structure, water, bare land and grass field are anticipated to consistently increase. Such results can be utilized as basic data to estimate the change in land cover according to climate change in order to prepare climate change response strategies.

• The Journal of Engineering Geology
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• v.23 no.4
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• pp.409-425
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• 2013

This study presents a prediction of a time-series of the area inundated by effluent from Heavenly Lake caused by ground behavior prior to a volcanic eruption. A GIS-based hydrological algorithm that considers the multi-flow direction of effluent, the absorption and storage capacity of the ground soil, the storage volume of the basin or the depression terrain, was developed. To analyze the propagation pattern, four hypothetical collapse zones on the cheonji ground were set, considering the topographical characteristics and distributions of volcanic rocks at Mt. Baekdu. The results indicate that at 3 hours after collapse, for both scenarios 1 and 2 (collapses of the entire/southern boundary of cheonji), a flood hazard exists for villages in China, but not for those on the North Korean side of the mountain, due to the topographical characteristics of Mt. Baekdu. It is predicted that villages in both North Korea and China would be significantly damaged by flood inundation at 3 hours elapsed time for both scenarios 3 and 4 (collapses on the southern boundary of cheonji and on the southeastern-peak area).

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.697-723
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• 2022

Data-driven models to predict groundwater levels 30 days in advance were developed for 12 groundwater monitoring stations in the middle-Jeju watershed, Jeju Island. Stacked long short-term memory (stacked-LSTM), a deep learning technique suitable for time series forecasting, was used for model development. Daily time series data from 2001 to 2022 for precipitation, groundwater usage amount, and groundwater level were considered. Various models were proposed that used different combinations of the input data types and varying lengths of previous time series data for each input variable. A general procedure for deep-learning-based model development is suggested based on consideration of the comparative validation results of the tested models. A model using precipitation, groundwater usage amount, and previous groundwater level data as input variables outperformed any model neglecting one or more of these data categories. Using extended sequences of these past data improved the predictions, possibly owing to the long delay time between precipitation and groundwater recharge, which results from the deep groundwater level in Jeju Island. However, limiting the range of considered groundwater usage data that significantly affected the groundwater level fluctuation (rather than using all the groundwater usage data) improved the performance of the predictive model. The developed models can predict the future groundwater level based on the current amount of precipitation and groundwater use. Therefore, the models provide information on the soundness of the aquifer system, which will help to prepare management plans to maintain appropriate groundwater quantities.