• 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.

• Economic and Environmental Geology
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• v.55 no.4
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• pp.407-419
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• 2022

Soil(vadose zone) gas compositions were measured for about 3 days to suggest a method for monitoring and interpreting soil gas data collected around wells from which methane(CH₄) is outflowing. The vadose zone gas samples were collected within 1 m around two test wells(TB2 and TB3) at Pohang and analyzed for CO₂, CH₄, N₂ and O₂ concentrations in situ. CO₂ flux was measured beside TB2. In addition, gas samples from well head in TB2 and atmospheric air samples were collected for comparison. Carbon isotopes of CO₂(δ¹³C_CO2) of samples collected on the last day of the study period were analyzed in the laboratory. The two test wells (TB2 and 3) were 12.7 m apart and only TB3 was cemented to the surface. According to the bio-geochemical process-based interpretation, the relationships between CO₂ and O₂, N₂, and N₂/O₂ of vadose zone gas were plotted between the lines of CH₄ oxidation and CO₂ dissolution. In addition, the CH₄ concentrations of gas samples from the wellhead of the uncemented well (TB2) were 5.2 times higher than the atmospheric CH₄ concentration. High CO₂ concentrations (average 1.148%) of vadose zone gas around TB2 seemed to be attributed to the oxidation of CH₄. On the other hand, the vadose zone CO₂ around the cemented well(TB3) showed a relatively low concentration(0.136%). This difference indicates that the vadose zone gas(including CO₂) around the CH₄ outflowing well were strongly affected by well completion(cementing). This study result can be used to establish strategies for environmental monitoring of soil around natural gas sites, and can be used to monitor leakage around injection and observation wells for CO₂ geological storage. In addition, the method of this study is useful for soil monitoring in natural gas storage and oil-contaminated sites.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.543-551
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• 2023

Snow is an essential climate factor that affects the climate system and surface energy balance, and it also has a crucial role in water balance by providing solid water stored during the winter for spring runoff and groundwater recharge. In this study, statistical analysis of Local Data Assimilation and Prediction System (LDAPS), Modern.-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and ERA5-Land snow depth data were used to evaluate the applicability in South Korea. The statistical analysis between the Automated Synoptic Observing System (ASOS) ground observation data provided by the Korea Meteorological Administration (KMA) and the reanalysis data showed that LDAPS and ERA5-Land were highly correlated with a correlation coefficient of more than 0.69, but LDAPS showed a large error with an RMSE of 0.79 m. In the case of MERRA-2, the correlation coefficient was lower at 0.17 because the constant value was estimated continuously for some periods, which did not adequately simulate the increase and decrease trend between data. The statistical analysis of LDAPS and ASOS showed high and low performance in the nearby Gangwon Province, where the average snowfall is relatively high, and in the southern region, where the average snowfall is low, respectively. Finally, the error variance between the four independent snow depth data used in this study was calculated through triple collocation (TC), and a merged snow depth data was produced through weighting factors. The reanalyzed data showed the highest error variance in the order of LDAPS, MERRA-2, and ERA5-Land, and LDAPS was given a lower weighting factor due to its higher error variance. In addition, the spatial distribution of ERA5-Land snow depth data showed less variability, so the TC-merged snow depth data showed a similar spatial distribution to MERRA-2, which has a low spatial resolution. Considering the correlation, error, and uncertainty of the data, the ERA5-Land data is suitable for snow-related analysis in South Korea. In addition, it is expected that LDAPS data, which is highly correlated with other data but tends to be overestimated, can be actively utilized for high-resolution representation of regional and climatic diversity if appropriate corrections are performed.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.2
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• pp.28-41
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• 2023

The objective of the study was to develop an Urban Windway Forest Creation Planning Technique for the Improvement of the Urban Environment using the case of Daejeon Metropolitan City. Through a spatial analysis of fine dust and heat waves, a basin zone, in which the concentration was relatively serious, was derived, and an area with the potential of cold air flow was selected as the target area for the windway forest development by analyzing the climate and winds in the relevant zone. Extreme fine dust areas included the areas of the Daejeon Industrial Complex Regeneration Business District in Daedeok-gu and Daedeok Techno Valley in Yuseong-gu. Heat wave areas included the areas of Daedeok industrial Complex in Moksang-dong, the Daejeon Industrial Complex Regeneration Business District in Daehwa-dong, and the high-density residential area in Ojeong-dong. As a result of measuring the wind speeds in Daejeon with an Automatic Weather System, the average wind speeds during the day and night were 0.1 to 1.7 m/s,, respectively. So, a plan of for a windway forest that smoothly induces the movement of cold air formed in outer forests at night is required. The fine dust/heat wave intensive management zones of Daejeon Metropolitan City were Daejeoncheon, Yudeungcheon, Gapcheon-Yudeungcheon, and Gapcheon. The windway forest formation plan case involved the old city center of Daejeon Metropolitan City among the four zones, the Gapcheon-Yudeungcheon area, in which the windway formation effect was presumed to be high. The Gapcheon-Yudeungcheon area is a downtown area that benefits from the cold and fresh air generated on Mt. Gyejok and Mt. Wuseong, which are outer forests. Accordingly, the windway forest was planned to spread the cold air to the city center by connecting the cold air generated in the Seosa-myeon forest of Mt. Gyejok and the Namsa-myeon forest of Mt. Wuseong through Gapcheon, Yudeungcheon, and street forests. After selecting the target area for the wind ventilation forest, a climate map and wind formation function evaluation map were prepared for the area, the status of variation wind profiles (night), the status of fine dust generation, and the surface temperature distribution status were grasped in detail. The wind ventilation forest planning concept and detailed target sites by type were identified through this. In addition, a detailed action plan was established according to the direction of creation and setting of the direction of creation for each type of wind ventilation forest.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.130-145
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• 2023

The digital transformation of finance is accelerating, and digital banking has already become a major banking channel. Banks have traditionally placed importance on CRM(Customer Relationship Management) and have tried to retain their loyal customers, who contribute significantly to the bank, such as long-term transactions, holding accounts with a certain balance or more, and holding loans. In this situation, this study exploratorily analyzed the consumer behavior of digital banking deposits in a major bank of Korea(1,145 samples). Statistical analysis was performed using SPSS. The main findings of the study are summarized as follows. It was found that there were differences of consumer behavior in digital banking deposits by generation, and the MZ generation used digital banking more on holidays than other generations. As a result of analyzing the behavior of existing loyal customers and regular customers of digital banking deposit, there was a significant difference in both the amount and period of the deposit. It was confirmed that the existing loyal customers of the bank also engage in consumer behavior that contributes to the bank in digital banking. In addition, the interaction between the customer type and the date of sign up for the deposit period, which is the goal setting of financial consumers, it was found that there was a significant effect. This study empirically analyzed the consumer behavior of digital banking in a situation where decrease of bank branches and encounters with digital banking. The major concepts of the consumer behavior theory are Loyal Customer, Goal Pursuit, and Habit, which were confirmed in an example of digital banking. The results of this study can suggest practical implications for existing banks and Internet-only banks, including the importance of customer management in digital banking.

• Journal of Korea Water Resources Association
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• v.57 no.5
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• pp.333-346
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• 2024

High-resolution medium-range streamflow prediction is crucial for sustainable water quality and aquatic ecosystem management. For reliable medium-range streamflow predictions, it is necessary to understand the characteristics of forcings and to effectively utilize weather forecast data with low spatio-temporal resolutions. In this study, we presented a comparative analysis of medium-range streamflow predictions using the distributed hydrological model, WRF-Hydro, and the numerical weather forecast Global Data Assimilation and Prediction System (GDAPS) in the Geumho River basin, Korea. Multiple forcings, ground observations (AWS&ASOS), numerical weather forecast (GDAPS), and Global Land Data Assimilation System (GLDAS), were ingested to investigate the performance of streamflow predictions with highresolution WRF-Hydro configuration. In terms of the mean areal accumulated rainfall, GDAPS was overestimated by 36% to 234%, and GLDAS reanalysis data were overestimated by 80% to 153% compared to AWS&ASOS. The performance of streamflow predictions using AWS&ASOS resulted in KGE and NSE values of 0.6 or higher at the Kangchang station. Meanwhile, GDAPS-based streamflow predictions showed high variability, with KGE values ranging from 0.871 to -0.131 depending on the rainfall events. Although the peak flow error of GDAPS was larger or similar to that of GLDAS, the peak flow timing error of GDAPS was smaller than that of GLDAS. The average timing errors of AWS&ASOS, GDAPS, and GLDAS were 3.7 hours, 8.4 hours, and 70.1 hours, respectively. Medium-range streamflow predictions using GDAPS and high-resolution WRF-Hydro may provide useful information for water resources management especially in terms of occurrence and timing of peak flow albeit high uncertainty in flood magnitude.