• Journal of Soil and Groundwater Environment
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• v.21 no.6
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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.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2000.11a
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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/).

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.04a
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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.

• Journal of Soil and Groundwater Environment
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• v.16 no.3
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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.

• Journal of Soil and Groundwater Environment
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• v.23 no.1
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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.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.09a
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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.

• 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.10 no.1
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• pp.65-74
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• 2005

National groundwater monitoring stations have been managed throughout the country by Korea Water Resources Corporation (KOWACO) in order to monitor variations in quantity and quality of groundwater resources. A multi-sensor installed in each monitoring station well measures groundwater level, water temperature and electrical conductivity every six hours and the logged data are automatically transmitted to a host computer in KOWACO. Meanwhile despite regular station inspection and replacement of deteriorate or broken devices, abnormal values or outliers often occur due to intrinsic limitations of automatic monitoring and transmission. Thus prompt recognition and measures to these values are essentially required to reduce disturbance and missing period of the data. In this study, time and frequency of outlier occurrence were analyzed for the water level data obtained from national groundwater monitoring stations within the Han river basin in 2000. The analysis results indicated that the most prominent patterns of the outliers were rapid decline for water level, no variation for temperature and steep decline for electrical conductivity. This study provided a sample criterion for determining the outlier for each parameter.

• Journal of Soil and Groundwater Environment
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• v.11 no.2
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• pp.56-67
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• 2006

Trends of variation in groundwater levels, electrical conductivities and water temperatures obtained from the national groundwater monitoring stations (95 shallow and 169 deep wells) of Korea were evaluated. For the analysis, both parametric (linear regression) and non-parametric (Mann-Kendall test, Sen's test) methods were adopted. Results of linear regression analysis indicated that about 50% of the monitoring wells showed increasing trends of groundwater levels, electrical conductivities, and water temperatures and the others showed decreasing trends. However, the non-parametric analyses with monthly median values revealed that $14.8{\sim}20.0%$ of water levels were decreased, $24.2{\sim}36.9%$ of electrical conductivities were increased, and $27.4{\sim}32.5%$ of water temperatures were increased at a confidence level of 99%. Highly proportions of increasing or decreasing trends were unexpected and they resulted from the relatively short term of data collection (maximum 6 years). Meanwhile, the investigation of groundwater around the national groundwater monitoring stations showed that the decreasing or increasing trends of water levels, electrical conductivities, themselves, didn't indicate directly groundwater hazards such as groundwater depletion or groundwater contamination. Both the values and variation rates (slopes) of water level, electrical conductivity and temperature in the longer period are considered simultaneously. This study is the first comprehensive work in analyzing trends of groundwater data obtained from the national groundwater monitoring stations. Based on this study, the periodical and regular analysis of groundwater data is essentially required to grasp the overall variational trend of groundwater resources in the country.

• Journal of Soil and Groundwater Environment
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• v.19 no.3
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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.