Review of earthquake-induced landslide modeling and scenario-based application

Giha Lee,Hyunuk An,Minho Yeon,Jun Pyo Seo,Chang Woo Lee 충남대학교 농업과학연구소 2020 Korean Journal of Agricultural Science Vol.47 No.4

Spatiotemporal Variation of Erosion and Deposition using the Catchment-scale Soil Erosion Model (CSEM)

Giha Lee 한국토양비료학회 2014 한국토양비료학회 학술발표회 초록집 Vol.2014 No.6

An Extended Model Evaluation Method under Uncertainty in Hydrologic Modeling

Giha Lee,Sangkuk Youn,Yeonsu Kim 한국지반환경공학회 2015 한국지반환경공학회논문집 Vol.16 No.5

This paper proposes an extended model evaluation method that considers not only the model performance but also the model structure and parameter uncertainties in hydrologic modeling. A simple reservoir model (SFM) and distributed kinematic wave models (KWMSS1 and KWMSS2 using topography from 250-m, 500-m, and 1-km digital elevation models) were developed and assessed by three evaluative criteria for model performance, model structural stability, and parameter identifiability. All the models provided acceptable performance in terms of a global response, but the simpler SFM and KWMSS1 could not accurately represent the local behaviors of hydrographs. Moreover, SFM and KWMSS1 were structurally unstable; their performance was sensitive to the applied objective functions. On the other hand, the most sophisticated model, KWMSS2, performed well, satisfying both global and local behaviors. KMSS2 also showed good structural stability, reproducing hydrographs regardless of the applied objective functions; however, superior parameter identifiability was not guaranteed. A number of parameter sets could result in indistinguishable hydrographs. This result indicates that while making hydrologic models complex increases its performance accuracy and reduces its structural uncertainty, the model is likely to suffer from parameter uncertainty.

Analysis of flood and landslide due to torrential rain in Seoul, Korea on July, 2011

Giha LEE 한국산업응용수학회 2011 한국산업응용수학회 학술대회 논문집 Vol.6 No.2

Multiple RS-485 interface management FPGA design for Power micro-metering

Giha Yoon,Jaein Kim,Geun-Yong Kim,Byunghee Son,Hark Yoo 전력전자학회 2019 ICPE(ISPE)논문집 Vol.2019 No.5

UART is most widely used communication method in various industrial applications because of its simple structure, low cost, and easy usability. In energy management and control applications, a power meter is installed on each distributed power lane in the power distribution panel and power quality data are measured using UART communication. However, the data acquisition time drastically increases as the number of power meters increases due to the sequential data transfer of UART communication. In this paper, we propose a fast power micro-metering technique using parallel RS-485 interfaces architecture. We have verified the effectiveness of the proposed technique through a testbed implementation.

Comparison of physics-based and data-driven models for streamflow simulation of the Mekong river

Lee Giha,Jung Sungho,Lee Daeeop 한국수자원학회 2018 한국수자원학회논문집 Vol.51 No.6

최근 기후변화 및 유역개발로 인하여 메콩강 유역의 수문환경이 급격히 변화하고 있으며, 메콩강을 공유하는 국가의 수재해 예방 및 지속가능한 수자원개발을 위해서는 메콩강 주요지점에서의 유량 정보의 분석 및 예측이 요구된다. 본 연구에서는 물리적 기반의 수문모형인 SWAT과 데이터기반 딥러닝 알고리즘인 LSTM을 이용하여 메콩강 하류 Kratie 지점의 유출모의를 수행하고, 유출모의 정확도 및 두 가지 방법론의 장 ․ 단점을 비교 ․ 분석한다. SWAT 모형의 구축을 위해 범용 입력자료(지형: HydroSHED, 토지이용: GLCF-MODIS, 토양: FAO-Soil map, 강우: APHRODITE 등)을 이용하였으며 warming-up 및 매개변수 보정 후 2003~2007년 일유량 모의를 수행하였다. LSTM을 이용한 유출모의의 경우, 딥러닝 오픈소스 라이브러리인 TensorFlow를 활용하여 Kratie 지점기준 메콩강 상류 10개 수위관측소의 두 기간(2000~2002, 2008~2014) 일수위 정보만을 이용하여 심층신경망을 학습하고, SWAT 모형과 마찬가지로 2003~2007년을 대상으로 Kratie 지점에 대한 일수위 모의 후 수위-유량관계곡선식을 이용하여 유출량으로 환산하였다. 두 모형의 모의성능 비교 ․ 검토를 위하여 모의기간에 대해 NSE (Nash-Sutcliffe Efficiency)을 산정한 결과, SWAT은 0.9, LSTM은 보다 높은 0.99의 정확도를 나타내는 것으로 분석되었다. 메콩강과 같은 대유역의 특정 지점에 대한 수문시계열 자료의 모의를 위해서는 다양한 입력자료를 요구하는 물리적 수문모형 대신 선행시계열자료의 변동성을 기억 ․ 학습하여 이를 예측에 반영하는 LSTM 기법 등 데이터기반의 심층신경망 모형의 적용이 가능할 것으로 판단된다. In recent, the hydrological regime of the Mekong river is changing drastically due to climate change and haphazard watershed development including dam construction. Information of hydrologic feature like streamflow of the Mekong river are required for water disaster prevention and sustainable water resources development in the river sharing countries. In this study, runoff simulations at the Kratie station of the lower Mekong river are performed using SWAT (Soil and Water Assessment Tool), a physics-based hydrologic model, and LSTM (Long Short-Term Memory), a data-driven deep learning algorithm. The SWAT model was set up based on globally-available database (topography: HydroSHED, landuse: GLCF-MODIS, soil: FAO-Soil map, rainfall: APHRODITE, etc) and then simulated daily discharge from 2003 to 2007. The LSTM was built using deep learning open-source library TensorFlow and the deep-layer neural networks of the LSTM were trained based merely on daily water level data of 10 upper stations of the Kratie during two periods: 2000~2002 and 2008~2014. Then, LSTM simulated daily discharge for 2003~2007 as in SWAT model. The simulation results show that Nash-Sutcliffe Efficiency (NSE) of each model were calculated at 0.9(SWAT) and 0.99(LSTM), respectively. In order to simply simulate hydrological time series of ungauged large watersheds, data-driven model like the LSTM method is more applicable than the physics-based hydrological model having complexity due to various database pressure because it is able to memorize the preceding time series sequences and reflect them to prediction.

한반도 고주파수 지진파 배경잡음과 기상현상과의 상관관계 분석

이기하(Giha Lee),홍태경(Tae-Kyung Hong) 대한지질학회 2019 대한지질학회 학술대회 Vol.2019 No.10

An Extended Model Evaluation Method under Uncertainty in Hydrologic Modeling

Lee, Giha,Youn, Sangkuk,Kim, Yeonsu Korean Geo-Environmental Society 2015 한국지반환경공학회논문집 Vol.16 No.5

This paper proposes an extended model evaluation method that considers not only the model performance but also the model structure and parameter uncertainties in hydrologic modeling. A simple reservoir model (SFM) and distributed kinematic wave models (KWMSS1 and KWMSS2 using topography from 250-m, 500-m, and 1-km digital elevation models) were developed and assessed by three evaluative criteria for model performance, model structural stability, and parameter identifiability. All the models provided acceptable performance in terms of a global response, but the simpler SFM and KWMSS1 could not accurately represent the local behaviors of hydrographs. Moreover, SFM and KWMSS1 were structurally unstable; their performance was sensitive to the applied objective functions. On the other hand, the most sophisticated model, KWMSS2, performed well, satisfying both global and local behaviors. KMSS2 also showed good structural stability, reproducing hydrographs regardless of the applied objective functions; however, superior parameter identifiability was not guaranteed. A number of parameter sets could result in indistinguishable hydrographs. This result indicates that while making hydrologic models complex increases its performance accuracy and reduces its structural uncertainty, the model is likely to suffer from parameter uncertainty.

Soil Loss Vulnerability Assessment in the Mekong River Basin

Hoang Thu Thuy,Giha Lee 한국지반환경공학회 2017 한국지반환경공학회논문집 Vol.18 No.1

The Mekong River plays an extremely important role in Southeast Asia. Flowing through six countries, including China, Myanmar, Thailand, Laos PDR, Cambodia, and Vietnam, it is a site of great biological and ecological diversity and the habitat of numerous species of fish. It also supports a very large population that lives along the river basin. Therefore, much attention has been focused on the giant Mekong River Basin, particularly, its soil erosion and sedimentation problems. In fact, many methods have been used to calculate and simulate these problems. However, in the case of the Mekong River Basin, the available data is limited because of the extreme size of the area (about 795,000 ㎢) and lack of equipment systems in the countries through which the Mekong River flows. In this study, we applied the Universal Soil Loss Equation (USLE) model in a GIS (Geographic Information System) framework to calculate the amount of soil erosion and sediment load during the selected period, from 1951 to 2007. The result points out dangerous areas, such as the Upper Mekong River Basin and 3S Basin (containing the Sekong, Sesan, and Srepok Rivers) that are suffering the serious consequences of soil erosion problems. Moreover, the present model is also useful for supporting river basin management in the implementation of sustainable management practices in the Mekong River Basin and other basins.

Soil Erosion Modeling in the 3S Basin of the Mekong River Basin

Hoang Thu Thuy,Giha Lee,Wansik Yu,Yongchul Shin 한국지반환경공학회 2017 한국지반환경공학회논문집 Vol.18 No.7

The 3S Basin is described as an important contributor in terms of many aspects in the Mekong River Basin in Southeast Asia. However, the 3S Basin has been suffering adverse consequences of changing discharge and sediment, which are derived from farming, deforestation, hydropower dam construction, climate change, and soil erosion. Consequently, a large population and ecology system that live along the 3S Basin are seriously affected. Accordingly, the calculating and simulating discharge and sediment become ever more urgent. There are many methods to simulate discharge and sediment. However, most of them are designed only during a single rainfall event and they require many kinds of data. Therefore, this study applied a Catchment-scale Soil Erosion model (C-SEM) to simulate discharge and sediment in the 3S Basin. The simulated results were judged with others references’s data and the observed discharge of Strung Treng station, which is located in the mainstream and near the outlet of the 3S Basin. The results revealed that the 3S Basin distributes 31% of the Mekong River Basin’s total discharge. In addition, the simulated sediment results at the 3S Basin’s outlet also substantiated the importance of the 3S Basin to the Mekong River Basin. Furthermore, the results are also useful for the sustainable management practices in the 3S Basin, where the sediment data is unavailable.