• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.75-89
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• 2023

As the Mekong River basin is a nationally shared river, it is difficult to collect precipitation data, and the quantitative and qualitative quality of the data sets differs from country to country, which may increase the uncertainty of hydrological analysis results. Recently, with the development of remote sensing technology, it has become easier to obtain grid-based precipitation products(GPPs), and various hydrological analysis studies have been conducted in unmeasured or large watersheds using GPPs. In this study, rainfall-runoff simulation in the Mekong River basin was conducted using the SWAT model, which is a quasi-distribution model with three satellite GPPs (TRMM, GSMaP, PERSIANN-CDR) and two GPPs (APHRODITE, GPCC). Four water level stations, Luang Prabang, Pakse, Stung Treng, and Kratie, which are major outlets of the main Mekong River, were selected, and the parameters of the SWAT model were calibrated using APHRODITE as an observation value for the period from 2001 to 2011 and runoff simulations were verified for the period form 2012 to 2013. In addition, using the ConvAE, a convolutional neural network model, spatio-temporal correction of original satellite precipitation products was performed, and rainfall-runoff performances were compared before and after correction of satellite precipitation products. The original satellite precipitation products and GPCC showed a quantitatively under- or over-estimated or spatially very different pattern compared to APHPRODITE, whereas, in the case of satellite precipitation prodcuts corrected using ConvAE, spatial correlation was dramatically improved. In the case of runoff simulation, the runoff simulation results using the satellite precipitation products corrected by ConvAE for all the outlets have significantly improved accuracy than the runoff results using original satellite precipitation products. Therefore, the bias correction technique using the ConvAE technique presented in this study can be applied in various hydrological analysis for large watersheds where rain guage network is not dense.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.148-148
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• 2022

Spatiotemporal precipitation data is one of the primary quantities in hydrological as well as climatological studies. Despite the fact that the estimation of these data has made considerable progress owing to advances in remote sensing, the discrepancy between satellite-derived precipitation product (SPP) data and observed data is still remarkable. This study aims to propose an effective deep learning model (DLM) for bias correction of SPPs. In which TRMM (The Tropical Rainfall Measuring Mission), CMORPH (CPC Morphing technique), and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) are three SPPs with a spatial resolution of 0.25o exploited for bias correction, and APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) data is used as a benchmark to evaluate the effectiveness of DLM. We selected the Mekong River Basin as a case study area because it is one of the largest watersheds in the world and spans many countries. The adjusted dataset has demonstrated an impressive performance of DLM in bias correction of SPPs in terms of both spatial and temporal evaluation. The findings of this study indicate that DLM can generate reliable estimates for the gridded satellite-based precipitation bias correction.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.68-68
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• 2021

Sesan강과 Srepok강은 베트남, 캄보디아, 라오스가 공유하는 3S강 유역 (Sesan강, Srepok강, Sekong강)의 일부로 연구 및 관리된다. 3S강 유역은 Mekong강의 중요한 지류이며 Mekong강 유역의 상당 부분을 구성한다(Mekong강 유역 면적의 10%, 연간 총 유출량의 20%). 베트남 측 Sesan강 유역의 면적은 11,255km²이고 Srepok강 유역 면적은 18,162km²이다. Sesan강과 Srepok 강의 상류는 베트남 중부 고원의 긴 산맥에 위치하고 있다. Sesan강과 Srepok강 유역은 기후변화에 따른 홍수, 가뭄, 어업 지속 가능성 감소, 퇴적 등 많은 문제와 도전에 직면 할 것으로 예측되고 있다. 본 연구에서는 World Bank의 "Viet Nam Mekong Integrated Water Resources Management (M-IWRM) Project의 일환으로 베트남 정부 차원에서 처음으로 구축한 수자원관리 의사결정지원 시스템인 "DSS-2S"를 활용하여, Sesan-Srepok강 유역의 강둑 침식 위험성을 분석하였다. DSS-2S는 MIKE Hydro Basin을 기반으로 SWAT모델, 수리모델, 하상변동 모델, 및 수질모델 등과 연계 하여 구축되었다. 2030 년을 목표 연도로 설정하고, 기후 변화 시나리오와 사회 경제적 발전을 기반으로 DSS-2S에 포함되어 있는 유사 이송 및 수리학적 모델을 활용하여 주요 하천 단면에서의 평균 유속과 하상 침식 양을 예측하였다. 유속 및 심부 침식 기준에 근거하여 강둑 침식 위험성을 분석하였다. 모델의 시뮬레이션 결과를 기반으로 강둑 침식 위험이 있는 강 구간은 고(高)유속과 높은 침식의 조합에 의해 결정되었다. 고위험 침식 예상지는 Sesan강 유역의 Dak Bla, Po Ko, 및 Se San강에 총 길이 73.5km에 걸쳐 발생 할 것으로 분석되었으며, 침식 위험이 매우 높은 지역은 Dak Bla 강에 총 길이 2,286m, Po Ko 강에 총 길이 5,096m 정도가 발생 하는 것으로 분석되었다. 강둑 세국을 유발할 수 있는 다양한 인자들을 고찰하였으며, 본 성과는 베트남 중앙 정부의 장기수 자원 종합계획 수립의 기본 자료로 활용 될 예정이다.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.441-441
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• 2002

In order to ensure a balance between economic development and a healthy Mekong Basin environment supporting natural resources diversity and productivity critical to the livelihood of its 65 million inhabitants, the Mekong River Commission (MRC) has been investigating the use of radar to remotely characterize and monitor the diversity, complexity, size and connectivity of the Basin's aquatic habitats. The PACRIM AIRSAR Mission provided an opportunity to evaluate the usefulness of radar technology to derive information for assessing, forecasting and mitigating possible cumulative and long-term impacts of development on the natural environment and the people's livelihood. This paper presents the results of mapping wetland cover types using multi-polarimetric radar for an area of the north-western corner of the Tonle Sap basin with data acquired from the AIRSAR Mission in September 2000. The implementation of a newly developed segmentation classification routine used to derive the image classification is described and the results of a fieldwork campaign to check the classification is presented.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.207-207
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• 2021

The Vietnamese Mekong Delta(VMD) covers an area of 62,250 km² in the lowest basin of the Mekong Delta where more than half of the country's total rice production takes place. In 2016, an estimated 1.29 million tonnes of Vietnam's rice were lost to the country's biggest drought in 90 year and particularly in VMD, at least 221,000 hectares of rice paddies were hit by the drought and related saltwater intrusion from the South China Sea. In this study, 3D numerical simulations using Delft3D hydrodynamic models with calibration and validation process were performed to examine flow characteristics, climate change scenarios, water level changes, and salinity concentrations in the nine major estuaries and coastal zones of VMD during the 21^st century. The river flows and their interactions with ocean currents were modeled by Delft3D and since the water levels and saltwater intrusion in the area are sensitive to the climate conditions and upstream dam operations, the hydrodynamic models considered discharges from the dams and climate data provided by the Coupled Model Intercomparison Project Phase 6(CMIP6). The models were calibrated and verified using observational water levels, salinity distribution, and climate change data and scenarios. The results agreed well with the observed data during calibration and validation periods. The calibrated models will be used to make predictions about the future salinity intrusion events, focusing on the impacts of sea level rise due to global warming and weather elements.

• Journal of Korea Port Economic Association
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• v.32 no.4
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• pp.193-209
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• 2016

The Mekong River is a river in the south-eastern part of the continent of Aisa. It flows through the countries of Thailand, Lao PDR, Cambodia, Myanmar, and Vietnam that are located in Indochina and are members of ASEAN. These countries are growing rapidly and many others have entered into these markets. As the number of manufactures has increased, logistics markets become very attractive to our logistics companies that want to expand their business. This study focuses on four countries, Lao PDR, Cambodia, Myanmar, and Vietnam, which have potential economic growth and shows the environment of logistics with current logistics infrastructure and related investment law and system. The goal of this study to provide, with strength, weakness, opportunities, and threats(SWOT) analysis, some strategies to enter 4 countries' logistics market with SWOT and the strategies are as follows; First, foreign direct investment in logistics is linked with logistics infrastructure projects. Our government should strengthen its role to find cooperation programs that make connect with logistics business. Second, a logistics company is better off in a consortium with other manufacturers or other logistics companies to ensure minimum cargo and reduce entry risks. Finally, the four countries' roles as a logistics bases need to divided according to their environments, to benefits of logistics connecting between India and China.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.121-121
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• 2022

한국과 미국은 2018년 8월에 발표한 메콩우호국(Friends of the Lower Mekong, FLM) "메콩지역 수자원 데이터 관리 및 정보공유 강화에 관한 공동성명"을 계기로 메콩유역의 실시간 수자원 변동 모니터링 및 분석과 수자원 데이터 공동활용 역량을 강화하여 효율적이고 과학적인 수자원관리 지원과 함께 한국의 신남방정책과 미국의 인도-태평양 전략 시너지효과를 극대화하고자 메콩 주변국 재해경감 및 수자원 데이터 활용 역량강화를 위한 글로벌 위성기반 수문자료의 생산·활용 및 홍수·가뭄 등의 수재해 분석기술을 개발하고 있다. 여기에는 한국 K-water의 물관리 기술과 미국 NASA, USACE의 위성활용 및 수자원분석 기술을 접목하여 메콩지역의 체계적인 물관리 및 재해로부터 안전성 확보 기여에 목표를 두고 연구를 진행 중에 있다. 본 연구에서는 전 세계적으로 광범위하게 활용되고 있는 미공병단(USACE, U.S. Army Corps of Engineers)의 HEC software 프로그램을 메콩 시범지역(pilot area)에 적용하여 수리/수문모델 구축을 진행하고 있다. 구축되는 모형은 유역 상류 댐의 연계 모의운영 및 하류 홍수분석이 동시 가능한 HEC-RTS(Real-Time Simulation)로 이는 HEC-HMS, -ResSim, -RAS와 -FIA 모형이 순차적으로 결합된 수리/수문 모델링 시스템이다. 모형의 시범적용 지역은 현지 메콩위원회(MRC, Mekong River Comission)의 의견 등을 반영, 메콩강 하류지역(Lower Mekong) 본류 유역에 위성자료 활용 및 준실시간(near real-time)으로 댐 모의운영 등을 고려할 수 있는 JingHong댐(중국 란창강 최하류)에서 라오스 Xayaburi댐(메콩강 최상류)까지의 구간을 선정하였으며, 전년도에는HEC-RTS 중 HMS(Hydrologic Modeling System) 모형 적용을 중심으로 가용한 위성자료(GPM IMERG)를 활용하여 과거 홍수사상에 대한 모의를 고려한 강우-유출모형의 구축을 완료하였다. 이에 연속하여 금년도에는 동일유역 내 하천 단면 등이 확보된 Chiang Saen 지점에서 Xayaburi 댐까지의 구간에 대해 RAS(River Analysis System)을 구축할 예정으로 구축된 RAS 모형은 HEC-RTS에 포함되어 메콩 시범지역의 종합적 수리/수문분석에 적용될 예정이다.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.1124-1125
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• 2005

• Parasites, Hosts and Diseases
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• v.56 no.1
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• pp.75-79
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• 2018

Adult specimens of Echinostoma ilocanum (Garrison, 1908) Odhner, 1911 (Trematoda: Echinostomatidae) were recovered from 2 riparian people who resided along the Mekong River in Savannakhet Province, Lao PDR. In fecal examinations done by the Kato-Katz technique, they revealed echinostome eggs together with eggs of Opisthorchis viverrini (and minute intestinal fluke eggs) and hookworms. To recover the adult flukes, they were treated with praziquantel 30-40 mg/kg in a single dose and purged with magnesium salts. A total of 658 adult fluke specimens were recovered from the 2 people; 456 from case 1 and 202 from case 2. Specimens from case 1 consisted of 335 echinostomes (301 E. ilocanum and 34 species undetermined), 120 O. viverrini, and 1 Haplorchis taichui, and those from case 2 consisted of 36 E. ilocanum, 134 O. viverrini, and 32 H. taichui. Thus, the number of E. ilocanum specimens was 337 in total (average per person, 168.5). From this study, it is suggested that foodborne intestinal flukes and liver flukes are highly prevalent along the Mekong River in Savannakhet Province. The present report describes for the first time human infections with E. ilocanum in Lao PDR.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1095-1107
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• 2020

Flood monitoring using satellite data has been constrained by obtaining satellite images for flood peak and accurately extracting flooded areas from satellite data. Deep learning is a promising method for satellite image classification, yet the potential of deep learning-based flooded area extraction using SAR data remained uncertain, which has advantages in obtaining data, comparing to optical satellite data. This research explores the performance of SegNet and U-Net on image segmentation by extracting flooded areas in the Khorat basin, Mekong river basin, and Cagayan river basin in Thailand, Laos, and the Philippines from Sentinel-1 A/B satellite data. Results show that Global Accuracy, Mean IoU, and Mean BF Score of SegNet are 0.9847, 0.6016, and 0.6467 respectively, whereas those of U-Net are 0.9937, 0.7022, 0.7125. Visual interpretation shows that the classification accuracy of U-Net is higher than SegNet, but overall processing time of SegNet is around three times faster than that of U-Net. It is anticipated that the results of this research could be used when developing deep learning-based flood monitoring models and presenting fully automated flooded area extraction models.