• Journal of Korea Water Resources Association
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• v.53 no.8
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• pp.627-636
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• 2020

Debris flow disasters primarily occur in mountainous terrains far from cities. As such, they have been underestimated to cause relatively less damage compared with other natural disasters. However, owing to urbanization, several residential areas and major facilities have been built in mountainous regions, and the frequency of debris flow disasters is steadily increasing owing to the increase in rainfall with environmental and climate changes. Thus, the risk of debris flow is on the rise. However, only a few studies have explored the characteristics of flooding and reduction measures for debris flow in areas designated as steep slopes. In this regard, it is necessary to conduct research on securing independent disaster prevention technology, suitable for the environment in South Korea and reflective of the topographical characteristics thereof, and update and improve disaster prevention information. Accordingly, this study aimed to calculate the amount of debris flow, depending on disaster prevention performance targets for regions designated as steep slopes in South Korea, and develop an independent model to not only evaluate the impact of debris flow but also identify debris barriers that are optimal for mitigating damage. To validate the reliability of the two-dimensional debris flow model developed for the evaluation of debris barriers, the model's performance was compared with that of the hydraulic model. Furthermore, a 2-D debris model was constructed in consideration of the regional characteristics around the steep slopes to analyze the flow characteristics of the debris that directly reaches the damaged area. The flow characteristics of the debris delivered downstream were further analyzed, depending on the specifications (height) and installation locations of the debris barriers employed to reduce the damage. The experimental results showed that the reliability of the developed model is satisfactory; further, this study confirmed significant performance degradation of debris barriers in areas where the barriers were installed at a slope of 20° or more, which is the slope at which debris flows occur.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.514-514
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• 2023

댐 건설은 하천의 유황, 유사량을 변화시켜 이·치수 및 생태계에 영향을 미치고 있다. 그러나 현재 댐 운영에 사용하고 있는 하천 기본계획은 10년마다 수립하고 있어 하천 지형 측량자료, 식생 및 수생태를 고려한 하천 관리 계획은 대부분 과거자료이다. 또한 홍수소통에 영향을 미치는 하도식생에 대한 연구가 거의 없어 최근 기후변화로 인해 빈번하게 발생하는 홍수 등 물재해에 적극적 대응이 어려운 실정이다. 따라서 최신의 하천 기초조사 자료 기반, '20년 댐 하류하천에 홍수가 발생한 4개 댐(대청, 합천, 용담, 섬진강댐)을 대상으로 하상변동, 식생 영향 등을 고려한 댐 직하류 하천관리방안을 마련하는 연구를 진행하였다. 최신 하천 기초조사 자료 확보를 위해 하천기본계획 등 문헌정보를 수집하고 댐 직하류의 수중부·육상부 측량, 유사량·하상재료 조사, 식생조사, 홍수흔적조사 등을 실시하였다. 확보한 기초자료를 바탕으로 하상변동 분석 모델 및 물리적 서식처 모의 모델을 통해 이수, 치수, 생태 측면으로 분석을 진행하였다. 이수 분석으로 HEC-RAS의 유사이송 분석을 통해 현재부터 20년 뒤의 하상변동을 분석하였다. 치수 분석으로 HEC-RAS 2D 등의 유사이송 분석을 사용하여 한 개의 홍수기 사상 발생 시 하상변동을 분석하였으며, 식생의 유무가 하상변동에 미치는 영향을 함께 분석하였다. 생태 분석으로는 PHABSIM을 사용하여 대상어종에 대한 하상변동 및 식생 유무에 따른 환경생태영향의 영향을 분석하였다. 분석을 바탕으로 각 대상 댐 직하류에 이수, 치수, 생태 측면의 하천관리방안을 수립하였다. 이수 분석을 통해 20년 뒤의 취양수장의 제약수위를 예측, 검토하고 취약구간 모니터링 구간을 산정하였다. 또한 침식과 퇴적이 크게 일어나는 구간을 산정하여 장·단기 하상변동 모니터링 구간을 제시하였다. 치수 분석을 통해 식생이 댐 하류에 미치는 영향을 분석하여 식생 저감 구간을 선정하는 등 적정 식생 밀도 조절 방안을 제시하였다. 또한 하상변동량이 큰 구간은 하상 안정 유지를 위한 수제 설치를 모델 결과를 통해 검토하였으며 제방 정비가 필요한 구간은 드론을 활용한 모니터링을 제안하였다. 생태 분석을 통해 대상 어종이 서식하기 가장 적합한 환경생태유량을 산정하고 댐 하류 하천의 자연성 회복을 위한 증가방류, 유사환원 등의 방안을 검토하였다.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.6
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• pp.198-210
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• 2022

The southern strait of Jeju is a divergence point of the Tsushima Warm Current (TWC), and it is the starting point of the thermohaline circulation in the waters of the Korean Peninsula, affecting the size and frequency of marine disasters such as typhoons and tsunamis, and has a very important oceanographic impact, such as becoming a source of harmful organisms and radioactively contaminated water. Therefore, for an immediate response to these maritime disasters, real-time ocean observation is required. However, compared to other straits, in the case of southern Jeju, such wide area marine observations are insufficient. Therefore, in this study, surface current field of the southern strait of Jeju was calculated using High-Frequency radar (HF radar). the large surface current field is calculated, and post-processing and data improvement are carried out through APM (Antenna Pattern Measurement) and FOL (First Order Line), and comparative analysis is conducted using actual data. As a result, the correlation shows improvement of 0.4~0.7 and RMSE of about 1~19 cm/s. These high-frequency radar observation results will help solve domestic issues such as response to typhoons, verification of numerical models, utilization of wide area wave data, and ocean search and rescue in the future through the establishment of an open data network.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.14 no.1
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• pp.65-75
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• 2002

The changes of sea bottom configuration, which may cause the coastal disasters, have been considered as social problems. It is obvious that the beach deformation is attributable to the sediment transport associated with erosion and siltation in coastal areas such esturies, channel and harbors. The prediction method and countermeasures far them, however, are not on the level of satisfaction, which indicates that make efforts should be made on developing them. Groin was constructed at Hak-Dong gravel beach to embark ship at 1996, as a result region of right of groin, severe erosion of beach is proceeding till now 1999. In this study, based on the field measurements, involved the one-line theory model which was selected for the prediction of shoreline change to prepare coastal protection methods of Hak-Deng gravel beach. Author found that the storaged sediment estimation model by Sonu and Beek(1971) is useful model at the Hak-Dong gravel beach by the use of topographical survey data from September, 1998 to September, 1999.

• Journal of the Korean Geotechnical Society
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• v.37 no.12
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• pp.117-125
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• 2021

The occurrence of ground subsidence and sinkhole in downtown areas, which threatens the safety of citizens, has been frequently reported. Among the various mechanisms of a sinkhole, soil erosion through the damaged part of the sewer pipe was found to be the main cause in Seoul. In this study, a random forest model for predicting the occurrence of sinkholes caused by damaged sewer pipes based on sewage pipe information was trained using the information on the sewage pipe and the locations of the sinkhole occurrence case in Seoul. The random forest model showed excellent performance in the prediction of sinkhole occurrence after the optimization of its hyperparameters. In addition, it was confirmed that the sewage pipe length, elevation above sea level, slope, depth of landfill, and the risk of ground subsidence were affected in the order of sewage pipe information used as input variables. The results of this study are expected to be used as basic data for the preparation of a sinkhole susceptibility map and the establishment of an underground cavity exploration plan and a sewage pipe maintenance plan.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.3
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• pp.178-185
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• 2013

Forest fires are expected to increase in severity and frequency under global climate change and thus better understanding of fire dynamics is critical for mitigation and adaptation. Researchers with different background, such as ecologists, physicists, and mathematical biologists, have developed various simulation models to reproduce forest fire spread dynamics. However, these models have limitations in the fire spreading because of the complicated factors such as fuel types, wind, and moisture. In this study, we suggested a simple model considering the wind effect and two different fuel types. The two fuels correspond to susceptible tree and resistant tree with different probabilities of transferring fire. The trees were randomly distributed in simulation space with a density ranging from 0.0 (low) to 1.0 (high). The susceptible tree had higher value of the probability than the resistant tree. Based on the number of burnt trees, we then carried out the sensitivity analysis to quantify how the forest fire patterns are affected by wind and tree density. The statistical analysis showed that the total tree density had greatest effect on the forest fire spreading and wind had the next greatest effect. The density of the susceptible tree was relatively lower factor affecting the forest fire. We believe that our model can be a useful tool to explore forest fire spreading patterns.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.261-261
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• 2020

물관리의 기본이 되는 연속적인 유량 자료 확보를 위해서는 정확도 높은 수위-유량 관계 곡선식 개발이 필수적이다. 수위-유량 관계곡선식은 모든 수문시설 설계의 기초가 되며 홍수, 가뭄 등 물재해 대응을 위해서도 중요한 의미를 가지고 있다. 그러나 일반적으로 유량 측정은 많은 비용과 시간이 들고, 식생성장, 단면변화 등의 통제특성(control)이 변함에 따라 구간분리, 기간분리와 같은 비선형적인 양상이 나타나 자료 해석에 어려움이 존재한다. 특히, 국내 하천의 경우 자연적 및 인위적인 환경 변화가 다양하여 지점 및 기간에 따라 세밀한 분석이 요구된다. 머신러닝(Machine Learning)이란 데이터를 통해 컴퓨터가 스스로 학습하여 모델을 구축하고 성능을 향상시키는 일련의 과정을 뜻한다. 기존의 수위-유량 관계곡선식은 개발자의 판단에 의해 데이터의 종류와 기간 등을 설정하여 회귀식의 파라미터를 산출한다면, 머신러닝은 유효한 전체 데이터를 이용해 스스로 학습하여 자료 간 상관성을 찾아내 모델을 구축하고 성능을 지속적으로 향상 시킬 수 있다. 머신러닝은 충분한 수문자료가 확보되었다는 전제 하에 복잡하고 가변적인 수자원 환경을 반영하여 유량 추정의 정확도를 지속적으로 향상시킬 수 있다는 이점을 가지고 있다. 본 연구는 머신러닝의 대표적인 알고리즘들을 활용하여 유량을 추정하는 모델을 구축하고 성능을 비교·분석하였다. 대상지역은 안정적인 수량을 확보하고 있는 한강수계의 거운교 지점이며, 사용자료는 2010~2018년의 시간, 수위, 유량, 수면폭 등 이다. 프로그램은 파이썬을 기반으로 한 머신러닝 라이브러리인 사이킷런(sklearn)을 사용하였고 알고리즘은 랜덤포레스트 회귀, 의사결정트리, KNN(K-Nearest Neighbor), rgboost을 적용하였다. 학습(train) 데이터는 입력자료 종류별로 조합하여 6개의 세트로 구분하여 모델을 구축하였고, 이를 적용해 검증(test) 데이터를 RMSE(Roog Mean Square Error)로 평가하였다. 그 결과 모델 및 입력 자료의 조합에 따라 3.67~171.46로 다소 넓은 범위의 값이 도출되었다. 그 중 가장 우수한 유형은 수위, 연도, 수면폭 3개의 입력자료를 조합하여 랜덤포레스트 회귀 모델에 적용한 경우이다. 비교를 위해 동일한 검증 데이터를 한국수문조사연보(2018년) 내거운교 지점의 수위별 수위-유량 곡선식을 이용해 유량을 추정한 결과 RMSE가 3.76이 산출되어, 머신러닝이 세분화된 수위-유량 곡선식과 비슷한 수준까지 성능을 내는 것으로 확인되었다. 본 연구는 양질의 유량자료 생산을 위해 기 구축된 수문자료를 기반으로 머신러닝 기법의 적용 가능성을 검토한 기초 연구로써, 국내 효율적인 수문자료 측정 및 수위-유량 곡선 산출에 도움이 될 수 있을 것으로 판단된다. 향후 수자원 환경 및 통제특성에 영향을 미치는 다양한 영향변수를 파악하기 위해 기상자료, 취수량 등의 입력 자료를 적용할 필요가 있으며, 머신러닝 내 비지도학습인 딥러닝과 같은 보다 정교한 모델에 대한 추가적인 연구도 수행되어야 할 것이다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.403-405
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• 2019

The risk mitigation service based on network provides monitoring of the risk event data to be inputted and analyses its big data to be stored in real time. Furthermore, it performs the analysis of the plant disease risk such as a red tide, and livestock disease risk such a food-and-mouth disease, avian influenza, and rinderpest, and provides the mitigation service. The standardization road map for risk mitigation is the real time acquisition monitoring of risk events, and mitigation service for the risks.

• Geophysics and Geophysical Exploration
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• v.21 no.1
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• pp.8-14
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• 2018

For the purpose of maintenance and prevention of earth fill dams against damage from natural hazards, automatic monitoring through various measuring instruments and resistivity survey has been carried out. Reservoirs and embankments have the structural vulnerability on the agricultural usages since most of them were built more than thirty years ago. The main aim to use monitoring method is to verify the safety and integrity of the dam. Resistivity survey can detect potential weaknesses, such as defective zones, anomalous seepages or internal erosion processes. Permanent resistivity monitoring systems were installed at a reservoir, which daily measurements have been taken every 6 hour. Using monitoring data for one year, anomalous seepage and structural defects were clarified for dam safety. Annual water level fluctuations are around 10 m. During their operation, reservoir dams are subject to a never-ending hydraulic load from the reservoir, which over the years may cause changes in the properties of the inner parts of the dam construction. Detailed analysis of the monitoring results was performed and showed that resistivities at most locations have been very stable over the full monitoring period excluding the effects of water fluctuation and seasons. To investigate the detectability of weak zone using the DC resistivity monitoring, numerical modeling with a simplified model for the drainage at a reservoir dam was also performed. The results showed that the seepage zone near drainage in a reservoir dam could be detected by resistivity response change.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.299-316
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• 2019

The purpose of this study was to analyze landslide susceptibility in the Pyeongchang area using Weight of Evidence (WOE) and Evidential Belief Function (EBF) as probability models and Artificial Neural Networks (ANN) as a machine learning model in a geographic information system (GIS). This study examined the widespread shallow landslides triggered by heavy rainfall during Typhoon Ewiniar in 2006, which caused serious property damage and significant loss of life. For the landslide susceptibility mapping, 3,955 landslide occurrences were detected using aerial photographs, and environmental spatial data such as terrain, geology, soil, forest, and land use were collected and constructed in a spatial database. Seventeen factors that could affect landsliding were extracted from the spatial database. All landslides were randomly separated into two datasets, a training set (50%) and validation set (50%), to establish and validate the EBF, WOE, and ANN models. According to the validation results of the area under the curve (AUC) method, the accuracy was 74.73%, 75.03%, and 70.87% for WOE, EBF, and ANN, respectively. The EBF model had the highest accuracy. However, all models had predictive accuracy exceeding 70%, the level that is effective for landslide susceptibility mapping. These models can be applied to predict landslide susceptibility in an area where landslides have not occurred previously based on the relationships between landslide and environmental factors. This susceptibility map can help reduce landslide risk, provide guidance for policy and land use development, and save time and expense for landslide hazard prevention. In the future, more generalized models should be developed by applying landslide susceptibility mapping in various areas.