• 한국지하수토양환경학회지:지하수토양환경
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• 제21권6호
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• pp.80-86
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• 2016

Earthquakes of M5.1, M5.8 and M4.5 occurred in September 12 and 19 respectively in Gyeongju, Gyeongbuk Province. Theses earthquakes inflated fears of people and highlighted necessity of detailed countermeasures because we have considered our country is safe to earthquakes. In the meanwhile, earthquake also impacts groundwater and thus it was recently reported that the Gyeongju Earthquakes affected groundwater there. This study evaluates daily groundwater data collected from five national groundwater monitoring stations (Geoncheon, Sannae, Oedong, Yangbuksin, Cheonbuk) in Gyeongju. The analysis revealed that only groundwater level of bedrock monitoring well hosted in andesite exhibited earthquake impact while no wells in the other four stations hosted in sedimentary rocks showed substantial responses to the earthquakes. This may be derived from the difference of seismic velocity of hosting rocks as well as epicenter distance. Special interest on groundwater monitoring is required to predict earthquakes as precursory phenomena.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2000년도 추계학술대회
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• pp.36-39
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• 2000

The Ministry of Construction and Transportation is going to establish 310 groundwater monitoring stations. 154 stations have been established and periodically managed since 1995. Most of stations have two monitoring boreholes which function is to monitor the unconsolidated and bedrock aquifer, and have the automatic monitoring equipment to observe groundwater level, temperature and hydraulic conductivity which are measured four times a day. Especially 44 stations are equipped with the Remote Terminal Unit. MOCT publish "an annual report of Groundwater monitoring stations" every year and everyone can get the monitoring data from Groundwater World web site(http://wamis.kowaco.or.kr/gww/)..kr/gww/).

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2003년도 총회 및 춘계학술발표회
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• pp.259-262
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• 2003

In the Republic of Korea, Ministry of Construction & Transportation and Korea Water Resources Corporation manage the national groundwater monitoring network at the 169 stations and will organize the supplementary groundwater monitoring network at the 10,000 stations by 2011 year. The method that organizes the monitoring network was developed using the Analytic Hierarchy Process with pairwise comparison. Several estimation factors for the estimating every district were selected to reflect each district conditions. Their weighting value was decided by pairwise comparison and questions to the experts about groundwater The optimal number of groundwater monitoring well was calculated through the developed method. To verify this method, groundwater was monitored in Jeonju city by way showing the example. The study area In Jeonju city needs 7 stations for the supplementary groundwater monitoring network. The results monitored in 7 stations inferred the groundwater level around the study area by Kriging. The mean of residual between inferred groundwater level value from Kriging and actual groundwater level is rather low. Furthermore, the mean and standard deviation of residual between inferred groundwater level change and actual groundwater change is much lower. The Fact that 7 monitoring stations are sufficient for observing the groundwater condition in the study area makes it possible for suggested monitoring number to be proper.

• 한국지하수토양환경학회지:지하수토양환경
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• 제16권3호
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• pp.38-47
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• 2011

Groundwater monitoring data from the National Groundwater Monitoring Stations, a total of 320 stations, were analyzed to identify the response of water level and quality to the Odaesan earthquake (M4.9) occurred in January 2007. Among the total of eight stations responded to the earthquake, five wells showed water-level decline, and in three wells, water level rose. In terms of recovery, water levels in four stations had recovered to the original level in five days, but not in the rest four wells. The magnitude of water-level change shows weak relations to the distance between the earthquake epicenter and the groundwater monitoring station. However, the relations to the transmissivities of monitored aquifer in the station with the groundwater change were not significant. To implement the earthquake monitoring system through the groundwater monitoring network, we still need to accumulate the long-term monitoring data and geostatistically analyze those with hydrogeological and tectonic factors.

• 한국지하수토양환경학회지:지하수토양환경
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• 제23권1호
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• pp.41-53
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• 2018

Comprehensive statistical analysis for the 127 groundwater monitoring stations in Jeju Island during 2005~2015 was carried out for the re-establishment of management groundwater level. Three probability distribution functions such as normal distibution, GEV (General Extreme Value) distribution, and Gumbel distribution were applied and the maximum likelihood method was used for parameter estimation of each distribution. AIC (Akaike information criterion) was calculated based on the estimated parameters to determine the proper probability distribution for all 127 stations. The results showed that normal distribution and Gumble distribution were found in 11 stations. Whereas GEV distribution were found in 105 stations, which covered most of groundwater monitoring stations. Therefore, confidence levels should be established in accord with the proper probability distribution when groundwater level management is determined.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2003년도 추계학술발표회
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• pp.199-201
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• 2003

The monitoring effectiveness not only depends on the effectiveness of the network, but also the costs of the network. Generally the costs of the monitoring network are mainly on the equipment and personnel; the implementation and maintenance; the observation and sample connection; the sample analysis; and the data storage and processing. The cost of the monitoring network can be expressed as a function of monitoring frequency because the monitoring method can be an automatic or a manual measurement. To determine the sampling frequency of subsidiary groundwater monitoring stations, time series data of national groundwater monitoring stations were used. The proposed optimal sampling frequency for subsidiary groundwater monitoring station is about 7 to 20 days and the average frequency is about 2 weeks.

• 한국지하수토양환경학회지:지하수토양환경
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• 제21권4호
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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.

• 한국지하수토양환경학회지:지하수토양환경
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• 제10권1호
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• pp.65-74
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• 2005

한국수자원공사는 전국을 대상으로 지하수의 수위 및 수질의 변동을 관측하고 국내 지하수자원의 보전 관리를 위한 기반 구축을 위하여 국가지하수 관측소를 설치${\cdot}$운영하고 있다. 자동 관측장비를 관측정 내에 설치하여 6시간 간격으로 지하수위, 온도, 전기전도도를 관측하고 있다. 주기적인 관측소 현장점검과 노후화된 혹은 고장난 관측장비 교체 및 수리 등의 작업에도 불구하고 자동 관측 및 전송의 내재적 한계로 인하여 부분적으로 이상값이 발생하고 있다. 그런데 이러한 이상값의 발생으로 인한 자료의 교란이나 관측 발생기간을 줄이기 위해서는 빠른 인지와 대처가 필요하다. 본 단보에서는 한강유역 국가 지하수 관측소 2000년도 지하수 관측 원자료에 대하여 이상값이 발생되는 시기와 빈도를 분석하였고 그 결과 지하수위는 급하강, 수온은 무변동 그리고 전기전도도는 급하강 이상이 가장 많이 발생하는 것으로 나타났다. 또한 각 해당항목에 대해 이상값 판단기준을 실례로 제시하였다.

• 한국지하수토양환경학회지:지하수토양환경
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• 제11권2호
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• pp.56-67
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• 2006

본 연구에서는 국가 지하수관측소에서 획득한 지하수위, 전기전도도 및 수온 관측자료에 대해 모수 및 비모수 경향 분석을 실시하였다. 분석대상은 2003년까지 3년 이상 모니터링을 실시하고 있는 관측소의 지하수 자료이며, 이에는 충적관측정 95개소와 암반관측정 169개소가 해당된다. 모수분석으로 일평균 및 월중앙값에 대해 선형회귀분석을, 그리고 비모수분석으로 월중앙값에 대해 Mann-Kendall test 및 Sen's test를 적용하였다. 선형회귀분석을 통해서는 약50%의 관측정에서 수위, 전기전도도 및 수온이 증가경향을 나타내었고 나머지 절반은 감소하는 것으로 나타났다. 그러나 월중앙값을 이용한 비모수 경향분석에서는 99% 신뢰수준에서 지하수위는 $14.8{\sim}20.0%$가 감소경향으로 나타났고, 전기전도도는 $24.2{\sim}36.9%$가 증가경향을 보였으며, 수온의 경우에는 $27.4{\sim}32.5%$가 증가경향을 보였다. 높은 비율의 관측정에서 증가 혹은 감소의 경향성을 보이는 것은 분석대상 기간이 상대적으로 짧은(최장 6년) 것에 기인한 결과일 수 있다. 한편 현장조사를 실시하여 평가한 결과에서 나타난 지하수위 혹은 전기전도도의 감소 혹은 증가경향 자체가 직접적인 지하수 장해를 의미하지는 않는다. 결국 장기적인 경향성과 더불어 해당 인자의 값 자체 및 감소율을 고려하여야 한다. 본 연구는 국가 지하수관측소 자동 측정자료에 대한 최초의 전면적인 경향분석 결과이다. 이번 연구사례를 토대로 국내 지하수 자원의 전체적인 변동상황을 파악하기 위해서는 정기적인 경향분석을 수행할 필요가 있다.

• 한국지하수토양환경학회지:지하수토양환경
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• 제19권3호
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• pp.123-133
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• 2014

It is important to predict the groundwater level fluctuation for effective management of groundwater monitoring system and groundwater resources. In the present study, three different time series models for the prediction of groundwater level in response to rainfall were built, those are transfer function noise model (TFNM), artificial neural network (ANN), and adaptive neuro fuzzy interference system (ANFIS). The models were applied to time series data of Boen, Cheolsan, and Hongcheon stations in National Groundwater Monitoring Network. The result shows that the model performance of ANN and ANFIS was higher than that of TFNM for the present case study. As lead time increased, prediction accuracy decreased with underestimation of peak values. The performance of the three models at Boen station was worst especially for TFNM, where the correlation between rainfall and groundwater data was lowest and the groundwater extraction is expected on account of agricultural activities. The sensitivity analysis for the input structure showed that ANFIS was most sensitive to input data combinations. It is expected that the time series model approach and results of the present study are meaningful and useful for the effective management of monitoring stations and groundwater resources.