• The Journal of Engineering Geology
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• v.33 no.2
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• pp.307-322
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• 2023

Rock-mass grouting plays a crucial role in the construction of dams and deep caverns, effectively preventing seepage in the foundations, enhancing stability, and mitigating hazards. Most rock grouting is affected by hydrogeological and rock engineering indices such as rock quality designation (RQD), rock mass quality (Q-value), geological strength index (GSI), joint spacing (Js), joint aperture (Ap), lugeon value (Lu), secondary permeability index (SPI), and coefficient of permeability (K). Therefore, accurate geological analysis of basic rock properties and guidelines for grouting construction are essential for ensuring safe and effective grouting design and construction. Such analysis has been applied in dam construction sites, with a particular focus on the geological characteristics of bedrock and the development of prediction methods for grout take. In South Korea, many studies have focused on grout injection materials and construction management techniques. However, there is a notable lack of research on the analysis of hydrogeological and rock engineering information for rock masses, which are essential for the development of appropriate rock grouting plans. This paper reviews the current state of research into the correlation between the grout take with important hydrogeological and rock engineering indices. Based on these findings, future directions for the development of rock grouting research in South Korea are discussed.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Economic and Environmental Geology
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• v.54 no.3
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• pp.353-364
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• 2021

Since the mid-twentieth century, geology has gradually evolved as an interdisciplinary context in South Korea. The journal of Economic and Environmental Geology (EEG) has a long history of over 52 years and published interdisciplinary articles based on geology. In this study, we performed a literature review using topic modeling based on Latent Dirichlet Allocation (LDA), an unsupervised machine learning model, to identify geological topics, historical trends (classic topics and emerging topics), and association by analyzing titles, keywords, and abstracts of 2,571 publications in EEG during 1968-2020. The results showed that 8 topics ('petrology and geochemistry', 'hydrology and hydrogeology', 'economic geology', 'volcanology', 'soil contaminant and remediation', 'general and structural geology', 'geophysics and geophysical exploration', and 'clay mineral') were identified in the EEG. Before 1994, classic topics ('economic geology', 'volcanology', and 'general and structure geology') were dominant research trends. After 1994, emerging topics ('hydrology and hydrogeology', 'soil contaminant and remediation', 'clay mineral') have arisen, and its portion has gradually increased. The result of association analysis showed that EEG tends to be more comprehensive based on 'economic geology'. Our results provide understanding of how geological research topics branch out and merge with other fields using a useful literature review tool for geological research in South Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.328-328
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• 2023

표준강수지수(Standardized Precipitation Index, SPI)는 강수량 변동의 정도를 표준화하여 나타낸 지수로, 가뭄 평가에 적용되고 있다. 일반적으로 SPI를 산정할 때는 월 단위의 시간 척도를 적용하며, 장기간의 가뭄에 대해 평가한다. 그러나 시간 척도가 길어질수록 가뭄 발생 후 가뭄을 감지하는 데 걸리는 시간이 더 길어지기 때문에 대처가 더욱 어려워진다. 또한, 기후변화로 인해 가뭄 빈도가 증가하고, 그 정도가 더욱 심화되면서 일 단위의 적용이 필요해지고 있다. 본 연구는 한반도 남부지역을 대상으로 일 단위의 SPI 적용을 위한 최소 시간 척도를 조사하였다. 대상 지역을 강원권, 수도권, 부울경, 대경권, 호남권, 충청권의 총 6개 지역으로 분리하여, 각 지역별, 계절별 최소 시간 척도를 조사하였다. SPI 산정을 위해 후보 분포형으로 Gumbel, Gamma, GEV, Loglogistic, Lognormal, Weibull을 적용하였으며, 시간 척도는 5일부터 365일까지 총 10개로 설정하였다. 본 연구에선 크게 적합도 검정과 정규성 검정으로 진행하였다. 적합도 검정에서는 Chi-square test를 적용하였으며, 이때 일 단위의 짧은 시간 척도를 적용할 경우 누가 강수 시계열의 값이 0으로, 0값이 시계열에 포함되면 SPI의 정확도가 떨어지는 문제가 발생하는데, 이를 보완하기 위해 누가 강수 시계열의 0값을 고려하였다. 마지막으로 각 후보 분포형을 적용하여 산정된 SPI가 표준정규분포에 합당한지를 검증하기 위해 Anderson-Darling test를 수행하였다. 결과적으로 대부분의 지역에서는 봄과 여름의 경우 최소 15일 정도의 시간 척도까지는 적용할 수 있을 것으로 판단되며, 겨울의 경우는 최소 30일 정도의 시간 척도를 적용해야 함을 확인하였다. 지역별로 차이가 크진 않지만, 이러한 연구 결과를 참고하여 각 지역별로 더 나은 가뭄 대책을 마련할 수 있을 것으로 기대된다.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1049-1056
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• 2023

The height of the atmospheric boundary layer indicates the peak developed when turbulence is generated by mixing heat and water vapor, and is generally determined through thermodynamic methods. Wind profilers produce atmospheric information from the scattering of signals sent into the atmosphere. A method for making the spectrum of turbulent components, turbulent kinetic energy dissipation rate, and refractive index structure coefficient was presented to determine the atmospheric boundary layer depth. Compared with the vertical distribution characteristics of potential temperature and specific humidity based on radiosonde data, the determination method of the atmospheric boundary layer height from wind profiler output was evaluated as very useful.

• Journal of the Korean Society for Railway
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• v.18 no.6
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• pp.558-566
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• 2015

In this study, quantitative environmental impact assessments of the VSL anchor and RB(Reaming and Bearing) anchor systems were conducted after a life-cycle assessment (LCA). In addition, improvements which reduce the adverse environmental effects of the RB anchor system were confirmed through comparisons with results with a VSL anchor system. Both results showed that water ecotoxicity and global warming are the most important in environmental influences. To determine the effect of reducing the RB anchor system environment, the result was normalized for the environmental impact category. Most items appeared to have been improved with regard to the RB anchor system. The most significant improvement was a 77% decrease in POC levels(photochemical oxidant creation). Greenhouse gas emissions, related to global warming, were decreased by 44%. It is expected that these quantitative environmental impact assessment results will serve as the basis of an anchor system for civil engineering and environmental impact assessments.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1869-1880
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• 2021

Korean Journal of Remote Sensing (KJRS), leading the field of remote sensing and GIS in South Korea for over 37 years, has published interdisciplinary research papers. In this study, we performed the topic modeling based on Latent Dirichlet Allocation (LDA), a probabilistic generative model, to identify the research topics and trends using 1) the whole articles, and 2) specific articles related to natural and environmental disasters published in KJRS by analyzing titles, keywords, and abstracts. The results of LDA showed that 4 topics('Polar', 'Hydrosphere', 'Geosphere', and 'Atmosphere') were identified in the whole articles and the topic of 'Polar' was dominant among them (linear slope=3.51 × 10^-3, p<0.05) over time. For the specific articles related to natural and environmental disasters, the optimal number of topics were 7 ('Marine pollution', 'Air pollution', 'Volcano', 'Wildfire', 'Flood', 'Drought', and 'Heavy rain') and the topic of 'Air pollution' was dominant (linear slope=2.61 × 10^-3, p<0.05) over time. The results from this study provide the history and insight into natural and environmental disasters in KRJS with multidisciplinary researchers.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.568-580
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• 2020

Calibrating a conceptual hydrologic model necessitates selection of a calibration period that produces the most reliable prediction. This often must be chosen randomly, however, since there is no objective guidance. Observation plays the most important role in the calibration or uncertainty evaluation of hydrologic models, in which the key factors are the length of the data and the hydro-climate conditions in which they were collected. In this study, we investigated the effect of the calibration period selected on the predictive performance and uncertainty of a model. After classifying the inflows of the Hapcheon Dam from 1991 to 2019 into four hydro-climate conditions (dry, wet, normal, and mixed), a conceptual hydrologic partitioning model was calibrated using data from the same hydro-climate condition. Then, predictive performance and post-parameter statistics were analyzed during the verification period under various hydro-climate conditions. The results of the study were as follows: 1) Hydro-climate conditions during the calibration period have a significant effect on model performance and uncertainty, 2) calibration of a hydrologic model using data in dry hydro-climate conditions is most advantageous in securing model performance for arbitrary hydro-climate conditions, and 3) the dry calibration can lead to more reliable model results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.1
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• pp.131-140
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• 2013

Due to the limitations of the existing indoor positioning system depending on the radio wave, at present, it is required to introduce a new method in order to improve the accuracy in indoor environment. Recently, bio-inspired technology has become the future core technology. Thus, this study examined the accurate positioning method applying the abilities that animals with homing instinct measure their position by searching geomagnetic field with the use of their biomagnets. In order to confirm the applicability of geomagnetic field, a new source for indoor positioning, this study separated the constituent materials and building structure and designed the structures that can carry the actual magnetic field sensor and the data collection module. Subsequently, this study investigated the applicability of geomagnetic field as a positioning source by establishing the positioning system of Fingerprint method. In performance evaluation of the positioning system, the geomagnetic strength-based positioning system was similar to or approximately 20 percent higher than the wireless LAN-based positioning system in the buildings with the existing wireless LAN. Thus, in the environment without infrastructure for indoor positioning, the geomagnetic, an independent earth resource, can make it possible to realize the indoor positioning.