• Journal of Soil and Groundwater Environment
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• v.21 no.4
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• pp.30-41
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• 2016

The amount of groundwater related data is drastically increasing domestically from various sources since 2000. To justify the more expansive continuation of the data acquisition and to derive valuable implications from the data, continued employments of sophisticated and state-of-the-arts statistical tools in the analyses and predictions are important issue. In the present study, we employed a well established machine learning technique of Gaussian Process Regression (GPR) model in the trend analyses of groundwater level for the long-term change. The major benefit of GPR model is that the model provide not only the future predictions but also the associated uncertainty. In the study, the long-term predictions of groundwater level from the stations of National Groundwater Monitoring Network located within Han River Basin were exemplified as prediction cases based on the GPR model. In addition, a few types of groundwater change patterns were delineated (i.e., increasing, decreasing, and no trend) on the basis of the statistics acquired from GPR analyses. From the study, it was found that the majority of the monitoring stations has decreasing trend while small portion shows increasing or no trend. To further analyze the causes of the trend, the corresponding precipitation data were jointly analyzed by the same method (i.e., GPR). Based on the analyses, the major cause of decreasing trend of groundwater level is attributed to reduction of precipitation rate whereas a few of the stations show weak relationship between the pattern of groundwater level changes and precipitation.

• Journal of Soil and Groundwater Environment
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• v.20 no.7
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• pp.112-120
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• 2015

Statistical trend analysis using the data from the National Groundwater Quality Monitoring Network (NGQMN) of Korea was conducted to establish a new groundwater quality management framework. Sen’s test, a non-parametric statistical method for trend analysis, was used to determine the linear trend of the groundwater quality data. The analysis was conducted at different confidence levels (i.e., at 70, 80, 90, 95, and 99% confidence levels) for three of groundwater quality parameters, i.e., nitrate-nitrogen, chloride, and pH, which have sufficient time series of the NGQMN data between 2007 and 2013. The results showed that different trends can be determined for different depths even for the same monitoring site and the numbers of wells having significant trends vary with different confidence levels. The wells with increasing or decreasing trends were far less than the wells with no trend. Chloride had more wells with increasing trend than other parameters. On the other hand, nitrate-nitrogen had the most wells with increasing trend and concentration exceeding 75% of the threshold values (TVs). Based on the methodology used for this study, we suggest including groundwater TVs and trend analysis to evaluate groundwater quality and to establish an advanced groundwater quality management framework.

• Journal of Soil and Groundwater Environment
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• v.26 no.6
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• pp.186-195
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• 2021

In Korea, groundwater quality is monitored through National Groundwater Quality Monitoring Network (NGQMN) administered by Ministry of Environment. For a given contaminant, compliance to groundwater quality standards is assessed on a annual basis by monitoring the number of incidents that concentration exceeds the regulatory limit. However, this approach provides only a fractional information about groundwater quality degradation, and more crucial information such as location and severity of the contamination cannot be obtained. For better groundwater quality management on a watershed, a more spatially informative and intuitive method is required. This study presents two statistical methods to convert point-wise monitoring data into information on groundwater quality status of a watershed by using a proposed grading scale. The proposed grading system is based on readily available reference standards that classify the water quality into 4 grades. The methods were evaluated with NO₃^-, Cl^-, and total coliform data in Geum River basin. The analyses revealed that groundwater in most watersheds of Geum River basin is good for domestic or/and drinking with no treatment. But, there was notable quality degradation in Bunam seawall and So-oak downstream standard watersheds contaminated by NO₃^- and Cl^-, respectively.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.6
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• pp.13-23
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• 2022

The purpose of this study was to analyze the impact of greenhouse cultivation area and groundwater level changes due to the water curtain cultivation in the greenhouse complexes, which are mainly situated along rivers where water resources are easy to secure. The groundwater observation network in Miryang, Gyeongsangnam-do, located downstream of the Nakdong River, was selected for the study area. We classified the groundwater monitoring well into the greenhouse (riverside) and field cultivation areas (plain and mountain) to compare the groundwater impact of water curtain cultivation in the greenhouse complex. The characteristics of groundwater level changes classified by terrain type were analyzed using the observed data. Riverside wells have significant permeability coefficients and are close to rivers, so they are greatly affected by river flow and precipitation changes so that water level shows a specific pattern of annual changes. Most plain wells do not show a constant annual change, but observation wells near small rivers and small-scale greenhouse cultivation areas sometimes show annual and daily changes in which the water level drops during winter. Compared to other observation wells, mountain wells do not show significant yearly changes in water level and show general characteristics of bedrock aquifer well with a low permeability coefficient.

• The Journal of Engineering Geology
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• v.23 no.2
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• pp.137-147
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• 2013

For the effective management of groundwater resources, it is necessary to predict groundwater level fluctuations in response to rainfall events. In the present study, time series models using artificial neural networks (ANNs) and support vector machines (SVMs) have been developed and applied to groundwater level data from the Gasan, Shingwang, and Cheongseong stations of the National Groundwater Monitoring Network. We designed four types of model according to input structure and compared their performances. The results show that the rainfall input model is not effective, especially for the prediction of groundwater recession behavior; however, the rainfall-groundwater input model is effective for the entire prediction stage, yielding a high model accuracy. Recursive prediction models were also effective, yielding correlation coefficients of 0.75-0.95 with observed values. The prediction errors were highest for Shingwang station, where the cross-correlation coefficient is lowest among the stations. Overall, the model performance of SVM models was slightly higher than that of ANN models for all cases. Assessment of the model parameter uncertainty of the recursive prediction models, using the ratio of errors in the validation stage to that in the calibration stage, showed that the range of the ratio is much narrower for the SVM models than for the ANN models, which implies that the SVM models are more stable and effective for the present case studies.

• Nuclear Engineering and Technology
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• v.44 no.6
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• pp.639-652
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• 2012

A three-dimensional discrete fracture network model was developed in order to simulate the hydraulic characteristics of a granitic rock mass at Korea Atomic Energy Research Institute (KAERI) Underground Research Tunnel (KURT). The model used a three-dimensional discrete fracture network (DFN), assuming a correlation between the length and aperture of the fractures, and a trapezoid flow path in the fractures. These assumptions that previous studies have not considered could make the developed model more practical and reasonable. The geologic and hydraulic data of the fractures were obtained in the rock mass at the KURT. Then, these data were applied to the developed fracture discrete network model. The model was applied in estimating the representative elementary volume (REV), the equivalent hydraulic conductivity tensors, and the amount of groundwater inflow into the tunnel. The developed discrete fracture network model can determine the REV size for the rock mass with respect to the hydraulic behavior and estimate the groundwater flow into the tunnel at the KURT. Therefore, the assumptions that the fracture length is correlated to the fracture aperture and the flow in a fracture occurs in a trapezoid shape appear to be effective in the DFN analysis used to estimate the hydraulic behavior of the fractured rock mass.

• Journal of Soil and Groundwater Environment
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• v.6 no.1
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• pp.33-43
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• 2001

A calculation technique which estimates natural recharge using groundwater level change was proposed and prepared with the existing techniques using groundwater recession curve during dry days. As a part of estimating natural groundwater recharge nation wide, the reliable data from the national groundwater monitoring network were used and the methodology was applied to the three sites which have enough data (Chungju, Jinju and Kwangju). For this study, seasonal variation of groundwater level change, an analysis of lagging time on groundwater level and cumulative precipitation, and a comparative study for groundwater recharge were conducted.

• Journal of Soil and Groundwater Environment
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• v.13 no.1
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• pp.67-76
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• 2008

In this study, a combined model of a water-table fluctuation and a soil moisture content model is proposed for the estimation of groundwater recharge rate at a given location. To evaluate the model, groundwater level data from 4 monitoring wells (Pohang Yeonil, Pohang Kibuk, Suncheon Oeseo, Hongcheon Hongcheon) of National Groundwater Monitoring Network from 1996 to 2005 and precipitation data of corresponding years are used. From the proposed methodology, the groundwater recharge rates are estimated to be from 0.5 to 61.4% for Hongcheon Hongcheon, from 1.1 to 27.4% for Pohang Yeonil, from 5.1 to 41.4% for Pohang Kibuk, and from 1.1 to 8.3% for Suncheon Oeseo. The magnitude of variation of the estimated recharge rate depends on the soil type observed near the stations. The groundwater fluctuation model used in this study includes precipitation as a unique source of water-table perturbation and there may exist corollary limitations. To improve the applicability of the proposed method, a capillary-water content constitutive model for unsaturated fractured rock media may be considered. The proposed recharge rate delineation method is physically based and uses minimum numbers of assumptions. The method may be used as a better substitute for the previous tools for delineating recharge rate of a location using water-table fluctuation method and contribute to national groundwater management plan. Further research on the spatial interpolation of the method is under progress.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.09a
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• pp.307-311
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• 2004

The unsaturated zone is a significant pathway of the surface contaminant movement and is a highly heterogeneous medium. Therefore, there are limitations in applying conventional convection-dispersion equation(CDE). Artificial neural network(ANN) is considered to be a versatile tool for approximating complex functions. For evaluating the applicability of ANN, numerical tests using ANN were conducted with training set generated by HYDRUS-2D which is based on CDE. The results represent that ANN can estimate the solute transport and the choice of network parameters and generation of training set patterns are important for efficient estimation.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2005.04a
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• pp.348-351
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• 2005

In this study, solute breakthrough curves through the unsaturated zone were predicted using artificial neural network (ANN) by numerical tests and laboratory experiments. In the numerical tests, applicability of ANN model to prediction of breakthrough curves was evaluated using synthetic data generated by HYDRUS-2D. An appropriate strategy of ANN application and input data form were recommended. The ANN model was validated by laboratory experiments comparing with HYDRUS-2D simulations. The results show that the ANN model can be an effective method for forecasting solute breakthrough curves through the unsaturated zone when hydraulic data are available.