• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.231-231
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• 2015

Rapid development in the upper reaches of the Mekong River, in the form of construction of large hydropower dams and reservoirs, large irrigation schemes, and rapid urban development, is putting water resources under stress. Many scientific reports have pointed out that cascade dams along the Mekong River lead to serious problems: not only hydrologically but also a decline of agricultural productivity due to a decrease of sediment supply in the Mekong Delta and a change of fish amount due to drastic change of the water environment. Cambodia and Vietnam, located in the lowest Mekong basin, are gravely affected by radical changes of hydrologic regime due to Mekong River developments. In particular, the Tonle Sap Lake in Cambodia is very sensitive to the flood cycle and flow variation of the Mekong River as well as inflow water quality from the Mekong River. More than 50% of Cambodian GDP depends on the primary industries such as agriculture, fishing, and forestry, and the Tonle Sap Lake plays an important role to support the national economy in Cambodia. In addition, Cambodian people usually take nourishment from the fish of Tonle Sap Lake. This research aims to assess the impacts of the Mekong river flow alternation on the hydrologic regime of the Mekong River - Tonle Sap Lake. We carried out rainfall-runoff-inundation simulation using CAESER-LISFLOOD for integrated water resource management in the Tonle Sap Basin and then analyze flood inundation variation of the Tonle Sap Lake due to the scenarios. Furthermore, the simulated inundation maps were compared to MODIS satellite images for model verification and hydrologic prediction.

• Journal of Korea Water Resources Association
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• v.52 no.2
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• pp.125-132
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• 2019

In this study, we developed a heavy rainfall risk impact matrix, which can be used to evaluate the influence of heavy rainfall risk, and propose a method to evaluate the impact of heavy rainfall risk. We evaluated the heavy rainfall risk impact for each receptor (Residential, Transport, Utility) on Sadang-dong using the heavy rainfall event on July 27, 2011. For this purpose, the potential risk impact was calculated by combining the impact level and the rainfall depth based on the grid. Heavy Rainfall Risk Impact was calculated by combining with Likelihood to predict the heavy rainfall impact, and the degree of heavy rainfall in the Sadang-dong area was analyzed and presented based on grid.

• Journal of Korea Water Resources Association
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• v.51 no.spc
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• pp.1057-1066
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• 2018

Climate change has been influenced on extreme precipitation events, which are major driving causes of flooding. Especially, most of extreme water-related disasters in Korea occur from floods induced by extreme precipitation events. However, future climate change scenarios simulated with Global Circulation Models (GCMs) or Reigonal Climate Models (RCMs) are limited to the application on medium and small size rivers and urban watersheds due to coarse spatial and temporal resolutions. Therefore, the current study introduces the state-of-the-art approaches and procedures of statistical downscaling techniques to resolve this limitation It is expected that the temporally downscaled data allows frequency analysis for the future precipitation and estimating the design precipitation for disaster prevention.

• Journal of the Korean Geosynthetics Society
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• v.19 no.4
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• pp.85-93
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• 2020

This study describes the results of laboratory model test on settlement of soil contaminated with heavy metals, in order to evaluate the applicability of VMS to the measurement of gound settlements generated during the purification of contaminated soil. The measurement results for settlement of contaminated soil were compared using a 3D-Visual Monitoring System (VMS) based on digital photogrammetry and a total station. The test result showed that the settlement of the soil contaminated with heavy metals occurred a lot in the experimental condition in which the hydrophilic filter was applied. The minimum and maximum error ranges of VMS were calculated as ±0.36mm and ±0.87mm, respectively, and the error of VMS was satisfied in all experimental conditions. The average error rate of VMS was lower in the hydrophilic filter condition than in the hydrophobic filter condition. Therefore, it was evaluated that VMS can be applied to measure the settlement of contaminated soil.

• Tunnel and Underground Space
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• v.31 no.4
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• pp.211-220
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• 2021

Tunnel collapse often occurs during deep underground tunneling (> 40 m depth) in South Korea. Natural cavities as well as water supply pipes, sewer pipes, electric power cables, artificial cavities created by subway construction are complexly distributed in the artificial ground in the shallow depths of the urban area. For deep tunnel excavation, it is necessary to understand the properties of the ground which is characterized by porous elements and various geological structures, and their influence on the stability of the ground. This study analyzed geological factors for risk assessment in deep excavation in South Korea based on domestic and overseas case study. As a result, a total of 7 categories and 38 factors were derived. Factors with high weights were fault and fault clay, differential stress, rock type, groundwater and mud inrush, uniaxial compressive strength, cross-sectional area of tunnel, overburden thickness, karst and valley terrain, fold, limestone alternation, fluctuation of groundwater table, tunnel depth, dyke, RQD, joint characteristics, anisotropy, rockburst and so forth.

• Journal of Environmental Science International
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• v.30 no.12
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• pp.1053-1065
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• 2021

In this study, the prediction technology of Hydrological Quantitative Precipitation Forecast (HQPF) was improved by optimizing the weather predictors used as input data for machine learning. Results comparison was conducted using bias and Root Mean Square Error (RMSE), which are predictive accuracy verification indicators, based on the heavy rain case on August 21, 2021. By comparing the rainfall simulated using the improved HQPF and the observed accumulated rainfall, it was revealed that all HQPFs (conventional HQPF and improved HQPF 1 and HQPF 2) showed a decrease in rainfall as the lead time increased for the entire grid region. Hence, the difference from the observed rainfall increased. In the accumulated rainfall evaluation due to the reduction of input factors, compared to the existing HQPF, improved HQPF 1 and 2 predicted a larger accumulated rainfall. Furthermore, HQPF 2 used the lowest number of input factors and simulated more accumulated rainfall than that projected by conventional HQPF and HQPF 1. By improving the performance of conventional machine learning despite using lesser variables, the preprocessing period and model execution time can be reduced, thereby contributing to model optimization. As an additional advanced method of HQPF 1 and 2 mentioned above, a simulated analysis of the Local ENsemble prediction System (LENS) ensemble member and low pressure, one of the observed meteorological factors, was analyzed. Based on the results of this study, if we select for the positively performing ensemble members based on the heavy rain characteristics of Korea or apply additional weights differently for each ensemble member, the prediction accuracy is expected to increase.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.425-440
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• 2023

Monitoring water surface has become one of the most prominent areas of research in addressing environmental challenges.Accurate and automated detection of watersurface in remote sensing imagesis crucial for disaster prevention, urban planning, and water resource management, particularly for a country where water plays a vital role in human life. However, achieving precise detection poses challenges. Previous studies have explored different approaches,such as analyzing water indexes, like normalized difference water index (NDWI) derived from satellite imagery's visible or infrared bands and using k-means clustering analysis to identify land cover patterns and segment regions based on similar attributes. Nonetheless, challenges persist, notably distinguishing between waterspectralsignatures and cloud shadow or terrain shadow. In thisstudy, our objective is to enhance the precision of water surface detection by constructing a comprehensive water database (DB) using existing digital and land cover maps. This database serves as an initial assumption for automated water index analysis. We utilized 1:5,000 and 1:25,000 digital maps of Korea to extract water surface, specifically rivers, lakes, and reservoirs. Additionally, the 1:50,000 and 1:5,000 land cover maps of Korea aided in the extraction process. Our research demonstrates the effectiveness of utilizing a water DB product as our first approach for efficient water surface extraction from satellite images, complemented by our second and third approachesinvolving NDWI analysis and k-means analysis. The image segmentation and binary mask methods were employed for image analysis during the water extraction process. To evaluate the accuracy of our approach, we conducted two assessments using reference and ground truth data that we made during this research. Visual interpretation involved comparing our results with the global surface water (GSW) mask 60 m resolution, revealing significant improvements in quality and resolution. Additionally, accuracy assessment measures, including an overall accuracy of 90% and kappa values exceeding 0.8, further support the efficacy of our methodology. In conclusion, thisstudy'sresults demonstrate enhanced extraction quality and resolution. Through comprehensive assessment, our approach proves effective in achieving high accuracy in delineating watersurfaces from satellite images.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.424-424
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• 2021

서울시 전역의 하수관로는 4개의 처리구역, 16개의 배수구역, 163개의 배수분구, 748개의 소구역으로 분할하여 관리되고 있다. 하지만 지선관로와 간선관로가 상이한 설계빈도로 계획되어 가장 작은 관리단위인 소구역에서도 일정한 방재성능을 기대하기 어려운 실정이다. 따라서 본 연구에서는 소구역을 동일한 토출구를 갖는 유역으로 재정의하여 서울시 전역을 799개의 소구역으로 재분할한 후, 소구역 단위로 하수관로 방재성능을 분석하였다. 서울시 하수관로 방재성능 분석을 위해 도시유출모형은 SWMM, 도시침수모형은 2DIS를 활용하였다. 도시유출모형에서는 113,286개의 관로와 106,097개의 맨홀을 적용하였으며, 수리시설물은 서울시 관내에 위치한 31개의 모든 빗물저류조와 121개의 빗물펌프장 중 가용 가능한 117개의 빗물펌프장을 적용하였다. 하수관로의 외수위 경계조건은 하천기본계획에서 제시한 방류하천의 기점홍수위를 반영하였다. 도시침수모형에서는 5m 단위의 고해상도 지형자료와 토지피복도를 적용하여 2차원 침수모의를 수행하였으며, 799개의 소구역에 대한 방재성능 분석을 수행하여 이를 시스템에 적용하였다. 서울시 하수관로 방재성능 분석 시스템에서는 799개의 소구역을 대상으로 소구역 정보, 하수관로 정보, 수방시설물 정보, 방재성능 정보를 제공하고 있다. 소구역 정보는 해당 소구역에 대한 기본 정보 및 해당 소구역에 위치한 수리시설물에 대한 기본 정보를 제공한다. 하수관로 정보에서는 관로 및 맨홀을 선택하여 상세정보를 확인할 수 있다. 수방시설물 정보에서는 빗물펌프장, 빗물저류조, 강우관측소, 하천수위관측소, 관로수위관측소에 대한 상세정보를 제공한다. 마지막으로 방재성능 정보에서는 시나리오별 침수결과를 제공하며, 포인트 단위 침수취약지역을 표출한다. 서울시 하수관로 방재성능 분석 시스템은 하수도 정책 입안과 하수도 관련 사업 추진 시 데이터 기반의 신속하고 효율적인 의사결정 업무를 지원할 수 있을 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.154-154
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• 2020

최근 국지성 호우가 빈번하게 발생하고 있고, 이로 인해 국내 도시지역 홍수피해 발생빈도와 피해규모가 증가하고 있다. 2010년, 2011년, 2018년에 서울에서는 홍수로 인한 침수피해가 크게 발생하여 많은 인명과 재산의 피해가 있었다. 이렇듯 도시지역은 타 지역에 비해 인구와 재산이 밀집되어 있어 홍수 취약성이 상대적으로 높은 지역이다. 국내에서는 홍수피해 저감을 위해 홍수예보를 발령하고 있다. 하지만 국내 홍수예보는 국가하천 및 지방하천의 주요 하천 구간에서만 실시되고 있어 이러한 하천에 접하지 않는 지역은 국가 홍수예보의 수혜를 받을 수 없다. 그렇기 때문에 각 지역에서는 홍수 대응을 위해 기상청의 호우특보 기준을 사용하고 있으며 이 기준은 전국적으로 동일하다는 특징이 있다. 하지만 각 도시지역은 과거 홍수피해에 따라 방재시설을 추가로 설치하거나 보수하고 있어 각 지역의 방재시설 현황 및 홍수에 대한 취약성 정도가 다른 상황이다. 그러므로 전국적으로 동일한 강우기준이 적용되어 발령되고 있는 호우 특보는 실제 각 도시지역의 방재현황이 고려되지 못한다는 문제가 있다. 이와 관련하여 과거 낙동강 지역을 대상으로 지역별 홍수위험도에 따른 홍수위험지수를 산정하고 검토한 연구가 수행된 바 있다. 본 연구에서는 각 도시지역의 방재 현황을 고려하여 강우기준을 보정할 수 있는 가중치를 산정하는 방안에 대해 제시하였다. 이를 위해 서울 지역 25개 기초지자체를 대상으로 연구를 진행하였으며, 홍수 취약성을 평가하기 위한 세부인자는 노출도, 민감도, 적응도로 구분하였다. 각 세부인자 별 가중치를 산정하기 위해서는 엔트로피 방법을 적용하였고, 산정된 결과를 이용하여 각 지역 별 가중치를 산정하기 위해서는 유클리드 거리 산정법을 적용하였다. 그 결과 각 지역의 방재 특성을 고려한 가중치를 산정할 수 있었으며 향후에는 지역 별 방재특성이 고려된 강우기준을 제시 및 적용성을 검토할 계획이다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.5
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• pp.927-940
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• 2017

Landslide has been a major disaster in Indonesia, and recent climate change and indiscriminate urban development around the mountains have increased landslide risks. Java Island, Indonesia, where more than half of Indonesia's population lives, is experiencing a great deal of damage due to frequent landslides. However, even in such a dangerous situation, the number of inhabitants residing in the landslide-prone area increases year by year, and it is necessary to develop a technique for analyzing landslide-hazardous and vulnerable areas. In this regard, this study aims to evaluate landslide susceptibility of Java, an island of Indonesia, by using GIS-based spatial prediction models. We constructed the geospatial database such as landslide locations, topography, hydrology, soil type, and land cover over the study area and created spatial prediction models by applying Weight of Evidence (WoE), decision trees algorithm and artificial neural network. The three models showed prediction accuracy of 66.95%, 67.04%, and 69.67%, respectively. The results of the study are expected to be useful for prevention of landslide damage for the future and landslide disaster management policies in Indonesia.