• 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.

• The Journal of Engineering Geology
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• v.24 no.4
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• pp.631-641
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• 2014

Characteristics of water-level changes in the Temporary Gulpocheon Discharge Channel were identified by observing and analyzing changes in the subterranean water level induced by hydrological stresses the underground aquifer. The subterranean water level refers to the level at which the pressure of subterranean water passing through the corresponding position has an equipotential value that is in equilibrium with the atmospheric pressure at that location. This water level is not fixed but changes in response to hydrological stress. It can be identified by repeatedly measuring the distance from the observation point to the surface of the subterranean water. The subterranean water-level change equation and the variance range of the hydrological curve of subterranean water over 24 hours at the Gimpo-Gimpo National Groundwater Monitoring Network (NGMN) were used as assessment factors. The variance characteristics of the subterranean water at the 18 monitoring system locations were classified into three impact, observational wish, and non-impact. The impact type accounted for 50% of the subterranean water of and accurately reflected the water-level changes due to hydrological stress, showing that distance is the major controlling factor. The observational wish type accounted for 27.8% of the subterranean water, and one of the two assessment factors did not meet the assessment factors. The nonimpact type accounted for 22.2% of the subterranean water. This type satisfied the two assessment factors and represents subterranean water-level changes response to precipitation.

• Economic and Environmental Geology
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• v.53 no.5
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• pp.619-629
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• 2020

The National Groundwater Monitoring Network (NGMN) in South Korea has been implemented in alluvial/ bedrock aquifers for efficient management of groundwater resources. In this study, aquifer types were reclassified with unconfined and confined aquifers based on water-level fluctuation and water quality characteristics. Principal component analysis (PCA) of water-level data from paired monitoring wells of alluvial/bedrock aquifers results in the principal components of both aquifers showing similar water-level fluctuation pattern. There was no significant difference in the rate of water-level rises responding to precipitations and in the NO₃-N concentrations between the alluvial and bedrock aquifers. In contrast, in the results classified with the hydrogeological type, the principal components of water level were different between unconfined and confined conditions. The water-level rises to precipitation events were estimated to be 4.6 (R²=0.8) in the unconfined and 2.1 (R²=0.4) in the confined aquifers, respectively, indicating less impact of precipitation recharge to the confined aquifer. The confined aquifers have the average NO₃-N concentration below 3 mg/L, implying the natural background level protected from the sources at surface. In summary, reclassification of aquifers into hydrogeological types clearly shows the differences between unconfined and confined aquifers in the water-level fluctuation pattern and NO₃-N concentrations. The hydrogeologic condition of aquifer could improve groundwater resource management by providing critical information on groundwater quantity through recharge estimation and quality for protection from potential contamination sources.