• Journal of the Korean earth science society
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• v.43 no.1
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• pp.165-175
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• 2022

Earthquakes have occurred owing to movements on a fault since several billion years ago. Research on the relationship between earthquakes and groundwater began in the 1960s in the United States, but related works, including hydrogeochemistry research, only began in the 2010s in South Korea. In this study, domestic studies on the relationship between earthquakes and groundwater until 2021 were collected from the Web of Science and characterized by subject area (groundwater level, hydrogeochemistry, combination of the two, and others). The results showed that the number of published articles per year was positively correlated with the 2011 Tohoku earthquake, 2016 Gyeongju earthquake, and 2017 Pohang earthquake, with the maximum numbers observed in 2011, 2018, 2019, and 2020. Most studies on the relationship between earthquakes and groundwater level addressed groundwater level fluctuations in the duration of the subject earthquake, with little consideration of the precursors. Groundwater level monitoring data, as well as hydrogeochemical information and microbial communities, may contribute to a more detailed understanding of groundwater flow and chemical reactions in bedrock caused by earthquakes. Therefore, the establishment of a national groundwater monitoring network for seismic monitoring and prediction is required.

• Economic and Environmental Geology
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• v.36 no.6
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• pp.431-440
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• 2003

A modeling was performed to predict the groundwater recovery in the vicinity of the waterway tunnel area using a groundwater flow model MODFLOW. The model was calibrated to reproduce measured groundwater levels and observed flow rates into the tunnel prior to lining, and then used for flow simulation under transient condition. Model predictions under steady-state condition revealed that if tunnel conductance had been reduced by 25% to 90%, groundwater levels would recover between 8% and 72.4% of their initial levels and flow into the tunnel will decrease between 5.5% and 82.7%. In case of 75% tunnel condutance ruduction in transient simulation. most of wells were predicted to recover within 20 years or so. The complete recovery for the wells with the groundwater level over 70 m was found to be impossible. For the 90% tunnel conductance reduction, all wells were found to be recovered within 15 years.

• Journal of the Korean GEO-environmental Society
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• v.15 no.6
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• pp.5-15
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• 2014

This study is to quantitatively evaluate the impact on surrounding groundwater of waterway tunnel excavation and cofferdam construction in which A-dam and B-dam, so prediction of groundwater fluctuation and tunnel lining installation was studied. As a result, drawdown of groundwater level during tunnel excavation and cofferdam construction occurred about 3.58 m in the tunnel shaft. The initial condition of groundwater level recovered by up to 90 % was simulated after the completed the construction of the tunnel and lining installation. Groundwater inflow in the tunnel evaluated was analyzed to have exceeding water design criteria of the tunnel. The groundwater inflow is reduced to maximum $0.006m^3/min/km$ after lining installation done in the tunnel, so effect of lining installation was evaluated as 93 % or more. Drawdown of about 0.04~0.31 m occurs in the houses and temples analysis of groundwater system of the surrounding area from construction. Drawdown has occurred nearly by considering annual groundwater level fluctuation of National Groundwater Observation Network.

• Economic and Environmental Geology
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• v.51 no.4
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• pp.393-400
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• 2018

Despite some skeptical views on the possibility of earthquake prediction, observation and evaluation of precursory changes have been continued throughout the world. In Korea, the public concern on the earthquake prediction has been increased after 2016 $M_L5.8$ and 2017 $M_L5.4$ earthquakes occurred in Gyeongju and Pohang, the southeastern part in Korea, respectively. In this study, the abnormal increase of groundwater level was observed before the 2016 $M_L5.8$ Gyeongju earthquake in a borehole located in 52 km away from the epicenter. The well was installed in the Yangsan fault zone, and equipped for the earthquake surveillance. The abnormal change in the well would seem to be a precursor, considering the hydrogeological condition and the observations from previous studies. It is necessary to set up a specialized council to support and evaluate the earthquake prediction and related researches for the preparation of future earthquake hazards.

• Ecology and Resilient Infrastructure
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• v.5 no.3
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• pp.125-133
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• 2018

Artificial neural network (ANN) is a powerful model to predict time series data and have been frequently adopted to predict groundwater level (GWL). Many researchers have also tried to improve the performance of ANN prediction for GWL in many ways. Dummies are usually used in ANN as input to reflect the seasonal effect on predicted results, which is necessary for improving the predicting performance of ANN. In this study, the effect of Dummy on the prediction performance was analyzed qualitatively and quantitatively using several graphical methods, correlation coefficient and performance index. It was observed that results predicted using dummies for ANN model indicated worse performance than those without dummies.

• Geotechnical Engineering
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• v.9 no.3
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• pp.67-76
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• 1993

Korea being composed of a number of mountains has been damaged and destroyed in lives and properties by the occurrence of many landslides during the wet seasons. Therefore, it is necessary to study the forecast system and risk analysis for the occurrence of landslides : the rise of groundwater levels due to rainfall is the main cause of landslides. In this paper, the autoregressive models are used to predict the grondwater levls using cases of both time invariant and time -varing autoregressive coefficients. In the former case, AR(1), AR(2), and AR(3) models are selected and their single-valued parameters are estimated to fit them to the observed groundwater level series. In the latter case, modified AR(1) and typical AR(2) models are used as process model and a discrete Kalman Filtering technique is utilized to estimate the parameters which are themselves a function of time. The results show that the real time forecast system using the time-varying autoregressive coefficinets as well as time -invariant AR model is good to predict the groundwater level in hillside slopes and we might get better result if we use the time-hourly rainfall intensity as well as the observed groundwater level.

• Tunnel and Underground Space
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• v.17 no.1 s.66
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• pp.66-74
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• 2007

The analysis of ground subsidence stability was conducted for the residential area located on the limestone corrosion zone. For the investigation of the cavity distribution in limestone region, various geophysical investigations such as electroresistivity tomography, electromagnetic prospecting are carried out. Geotechnical field tests with drilling are also carried out for the evaluation of the ground characteristics. Based upon their results, numerical modeling is performed for the simulation and prediction of the ground subsidence with the conditions of cavity geometry and groundwater level. The main factor to cause the ground subsidence is estimated as the draw down of the groundwater level below soil overburden, which disturbs the mechanical equilibrium of ground and drives washing away the overburden soil through the cavity and solace subsidence. It seemed that it is essential to maintain the groundwater level continuously above the shallow cavity for the prevention of the ground subsidence on the limestone corrosion zone.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.697-723
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• 2022

Data-driven models to predict groundwater levels 30 days in advance were developed for 12 groundwater monitoring stations in the middle-Jeju watershed, Jeju Island. Stacked long short-term memory (stacked-LSTM), a deep learning technique suitable for time series forecasting, was used for model development. Daily time series data from 2001 to 2022 for precipitation, groundwater usage amount, and groundwater level were considered. Various models were proposed that used different combinations of the input data types and varying lengths of previous time series data for each input variable. A general procedure for deep-learning-based model development is suggested based on consideration of the comparative validation results of the tested models. A model using precipitation, groundwater usage amount, and previous groundwater level data as input variables outperformed any model neglecting one or more of these data categories. Using extended sequences of these past data improved the predictions, possibly owing to the long delay time between precipitation and groundwater recharge, which results from the deep groundwater level in Jeju Island. However, limiting the range of considered groundwater usage data that significantly affected the groundwater level fluctuation (rather than using all the groundwater usage data) improved the performance of the predictive model. The developed models can predict the future groundwater level based on the current amount of precipitation and groundwater use. Therefore, the models provide information on the soundness of the aquifer system, which will help to prepare management plans to maintain appropriate groundwater quantities.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.903-911
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• 2022

Groundwater, one of the resources for supplying water, fluctuates in water level due to various natural factors. Recently, research has been conducted to predict fluctuations in groundwater levels using Artificial Neural Network (ANN). Previously, among operators in ANN, Gradient Descent (GD)-based Optimizers were used as Optimizer that affect learning. GD-based Optimizers have disadvantages of initial correlation dependence and absence of solution comparison and storage structure. This study developed Gradient Descent combined with Harmony Search (GDHS), a new Optimizer that combined GD and Harmony Search (HS) to improve the shortcomings of GD-based Optimizers. To evaluate the performance of GDHS, groundwater level at Icheon Yullhyeon observation station were learned and predicted using Multi Layer Perceptron (MLP). Mean Squared Error (MSE) and Mean Absolute Error (MAE) were used to compare the performance of MLP using GD and GDHS. Comparing the learning results, GDHS had lower maximum, minimum, average and Standard Deviation (SD) of MSE than GD. Comparing the prediction results, GDHS was evaluated to have a lower error in all of the evaluation index than GD.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.