• Journal of Korean Society on Water Environment
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• v.26 no.6
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• pp.954-962
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• 2010

The objective of this study was to develop automatic event detection algorithm for groundwater level rise. The groundwater level data and rainfall data in July and August at 37 locations nationwide were analyzed to develop the algorithm for groundwater level rise due to rainfall. In addition, the algorithm for groundwater level rise by ice melting and ground freezing was developed through the analysis of groundwater level data in January. The algorithm for groundwater level rise by rainfall was composed of three parts, including correlation between previous rainfall and groundwater level, simple linear regression analysis between previous rainfall and groundwater level, and diagnosis of groundwater level rise due to new rainfall. About 49% of the analyzed data was successfully simulated for groundwater level rise by rainfall. The algorithm for groundwater level rise due to ice melting and ground freezing included graphic analysis for groundwater level versus time (day), simple linear regression analysis for groundwater level versus time, and diagnosis of groundwater level rise by new ice melting and ground freezing. Around 37% of the analyzed data was successfully simulated for groundwater level rise due to ice melting and ground freezing. The algorithms from this study would help develop strategies for sustainable development and conservation of groundwater resources.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.09a
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• pp.113-114
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• 2004

In order to classify the groundwater recharge characteristics in an urban area, a time series analysis of groundwater level data was performed. For this study, the daily groundwater level data from 35 monitoring wells were collected for 3 years (Fig. 1). The use of the cross-correlation function (CCF), one of the time series analysis, showed both the close relationship between rainfall and groundwater level change and the lag time (delay time) of groundwater level fluctuation after a rainfall event. Based on the result of CCF, monitored wells were classified into two major groups. Group I wells (n=10) showed a fast response of groundwater level change to rainfall event, with a delay time of maximum correlation between rainfall and groundwater level near 1 to 7 days. On the other hand, the delay time of 17-68 days was observed from Group II wells (n=25) (Fig. 1). The fast response in Group I wells is possibly caused by the change of hydraulic pressure of bedrock aquifer due to the rainfall recharge, rather than the direct response to rainfall recharge.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.09a
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• pp.565-569
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• 2003

One-year-long groundwater-level data have been collected from 18 wells in Cheon-an area. The result of barometric efficiency, autocorrelation, cross-correlation and statistical distribution evaluated from the measurement data shows that groundwater-level measurements from observation wells are the principal source of information about aquifer characteristics. Data from WA-2 has high barometric efficiency as well as steady decreasing auto-correlation coefficient, which means nonleaky confined aquifer, Most aquifers in this study show the unconfined properties so that barometric efficiencies are mostly low and the coefficients of cross-correlation between groundwater-level and precipitation are commonly high. This study showed that the long-term groundwater-level monitoring data without artificial stress such as pumping would give accurate information about aquifer characteristics.

• The Journal of Engineering Geology
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• v.18 no.1
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• pp.1-6
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• 2008

Precipitation and groundwater level data sets from Kum river watershed were analyzed and compared. The correlation between groundwater level and the moving average of precipitation was analyzed. Moving averaging technique is stochastic method and that was used to consider the effect of precipitation events on groundwater level fluctuation. Groundwater level generally follows seasonal precipitation pattern and low level occurs from early December to late April. Relatively high groundwater level is appeared in wet spell (July and August). The correlation between groundwater level and the moving average of precipitation to consider precedent precipitation events was analyzed with minimum two-year data sets. When the precipitation and groundwater level data set pair was selected the precipitation gauge station is closely located to groundwater level gauge station in the upstream direction to minimize the non-homogeneous precipitation distribution effect. The maximum correlation was occurred when the averaging periods were from 10 days to 150 days with Kum river watershed data. The correlation coefficients are influenced by data quality, missing data periods, or snow melt effect, etc. The maximum coefficient was 0.8886 for Kum river watershed data.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1151-1154
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• 2008

The water level data sets among hydrologic observation data are correspond to the hydraulic head for each observation point and determine flow direction. The level difference among reservoir, river, and groundwater determines groundwater flow direction, just like water flows in the downstream direction because the water level of upstream point is higher than that of downstream point. We can analyze the relationship among the components in hydrologic cycle by comparing the water level differences. This research dealt with the data from Nakdong river watershed in Gyungsangnam-Do. Three data group are used for the analysis and onr group is composed of reservoir, river, and groundwater data sets. The data sets are closely(within 10 km) located in the interested area.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.04a
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• pp.63-66
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• 2003

This study applied a hydrogeological field survey and isotope investigation to identify source locations and delineate pathways of groundwater contamination by nitrogen compounds. The infiltration and recharge processes were analyzed with groundwater-level fluctuation data and oxygen-hydrogen stable isotope data. The groundwater flow pattern was investigated through groundwater flow modeling and spatial and temporal variation of oxygen isotope data. Based on the flow analysis and nitrogen isotope data, source types of nitrate contamination in groundwater are identified. Groundwater recharge largely occurs in spring and summer due to precipitation or irrigation water in rice fields. Based on oxygen isotope data and cross-correlation between precipitation and groundwater level changes, groundwater recharge was found to be mainly caused by irrigation in spring and by precipitation at other times. The groundwater flow velocity calculated by a time series of spatial correlations, 231 m/yr, is in good accordance with the linear velocity estimated from hydrogeologic data. Nitrate contamination sources are natural and fertilized soils as non-point sources, and septic and animal wastes as point sources. Seasonal loading and spatial distribution of nitrate sources are estimated by using oxygen and nitrogen isotopic data.

• Journal of Environmental Science International
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• v.30 no.7
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• pp.519-527
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• 2021

Moving average precipitation provides periodic precipitation patterns by solving precipitation irregularities. However, due to uncertain moving average periods, excessive data smoothing occurs, which limit the possibility to analyze groundwater levels in the short term. Nonetheless, groundwater level fluctuation can compensate these limitations as it can calculate appropriately for unit time and verify the effect of precipitation penetrated into groundwater in a short time period. In this study, the characteristics of groundwater level were evaluated using groundwater level fluctuation to compensate for limitations of groundwater level analysis using moving average precipitation. In addition, the groundwater quality was investigated using the electrical conductivity fluctuation. The study site was Hyogyo-ri, Yesan-si, Chungcheongnam-do. Four observation wells and an automated weather system were used. The correlation between groundwater level fluctuation and precipitation (Case 1) and the correlation between groundwater level and moving average precipitation (Case 3) were compared. In the analysis for 1 hour data, the correlation coefficient of Case 1 was higher than that of Case 3, and in the analysis for 1 day data, the correlation coefficient of Case 3 was higher than that of Case 1.

• Journal of Soil and Groundwater Environment
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• v.20 no.3
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• pp.74-82
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• 2015

A method to filter out the effect of river stage fluctuations on groundwater level was designed using an artificial neural network-based time series model of groundwater level prediction. The designed method was applied to daily groundwater level data near the Gangjeong-Koryeong Barrage in the Nakdong river. Direct prediction time series models were successfully developed for both cases of before and after the barrage construction using past measurement data of rainfall, river stage, and groundwater level as inputs. The correlation coefficient values between observed and predicted data were over 0.97. Using the time series models the effect of river stage on groundwater level data was filtered out by setting a constant value for river stage inputs. The filtered data were applied to the hybrid water table fluctuation method in order to estimate the groundwater recharge. The calculated ratios of groundwater recharge to precipitation before and after the barrage construction were 11.0% and 4.3%, respectively. It is expected that the proposed method can be a useful tool for groundwater level prediction and recharge estimation in the riverside area.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.84-89
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• 2023

In water resource management, data prediction is performed using artificial intelligence, and companies, governments, and institutions continue to attempt to efficiently manage resources through this. LSTM is a model specialized for processing time series data, which can identify data patterns that change over time and has been attempted to predict groundwater level data. However, groundwater level data can cause sen-sor errors, missing values, or outliers, and these problems can degrade the performance of the LSTM model, and there is a need to improve data quality by processing them in the pretreatment stage. Therefore, in pre-dicting groundwater data, we will compare the LSTM model with the MSE and the model after normaliza-tion through distribution, and discuss the important process of analysis and data preprocessing according to the comparison results and changes in the results.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2002.04a
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• pp.351-356
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• 2002

Groundwater recharge rate was estimated by applying the groundwater level fluctuation method utilizing Theis (1937) approach with specific yield estimation technique of Shevenell (1996) and the temperature method using observed data from National Groundwater Observation Stations. Results based on analysis of water level observation data of 10 alluvium wells reveal that the recharge rates for 5 wells of Kum river area range 3.7~25.0% and those for 5 wells of Nakdong river area range 3.6~21.7%. Results obtained from the temperature method based on water temperature data indicated that the upward flow resulted from evapotranspiration is dominant for 4 wells of the Kum river area and 5 wells of the Nakdong river area. The other wells showed the downward flow which is related to groundwater recharge in these areas.