• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.149-149
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• 2018

Prediction on initial motion of sediment is crucial to evaluate sediment transport and channel stability. The condition of incipient movement of sediment is characterized by bed shear stress, which is generated from force of moving water against the bed of the channel, and by critical shear stress, which depends on force resisting motion of sediment due to the submerged weight of the grains. When the bed shear stress exceeds the critical shear stress, sediment particles begin rolling and sliding at isolated and random locations. In Mountain River, debris flow frequently occurs due to heavy rainfall and can lead some natural stones from mountain slope into the bed river. This phenomenon could add additional forces to sediment transport system in the bed of river and also affect or change direction and magnitude of sediment movement. In this paper, evaluations on incipient motion of uniform coarse gravel under falling spheres impacts using small scale flume channel were conducted. The drag force of falling spheres due to water flow and length movement of falling spheres were investigated. The experiments were carried out in flume channel made by glass wall and steel floor with 12 m long, 0.6 m wide, and 0.6 m deep. The bed slopes were selected with the range from 0.7% to 1.5%. The thickness of granular layer was at least 3 times of diameter of granular particle to meet grain placement condition. The sphere diameters were chosen to be 4cm, 6 cm, 8 cm, 10 cm. The spheres were fallen in to the bed channel for critical condition and under critical condition of motion particle. Based on the experimental results, the Shields curve of particles Reynold number and dimensionless critical shear stress were plotted. The relationship between with drag force and the length movement of spheres were plotted. The pathways of the bed material Under the impact of spheres falling were analyzed.

• 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.54 no.7
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• pp.545-552
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• 2021

The burden of flood control on the dam under frequently flood due to climate change and especially heavy flood in 2020 year are come to the forward and increased. The objective of the study is therefore to establish the method to estimate capacity and target water level for flood control in actual dam management. Frequency matching method was applied to establish a pair of cumulative distribution function (CDF) based on daily dam inflow and discharge records. The relationship between dam storage and discharge volume represented as a percentage of inflow volume was derived and its characteristics was analyzed. As the result, the Soyanggang (45%) and Chungju Dam (39%) contributing to flood control with temporarily storing flood runoff. The method and diagram to estimate flood control capacity and target water level for flood control in the dam were established. The result of the study could be used as a supplementary data for flood control of the dam according to the rainfall prediction on the Korea Meteorological Administration.

• Journal of Korean Society on Water Environment
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• v.27 no.3
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• pp.264-272
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• 2011

The objective of this study was to develop a model for predicting long-term runoff in a basin using the ensemble streamflow prediction (ESP) technique and review its reliability. To achieve the objective, this study improved not only the ESP technique based on the ensemble scenario analysis of historical rainfall data but also conventional ESP techniques used in conjunction with qualitative climate forecasting information, and analyzed and assessed their improvement effects. The model was applied to the Geum River basin. To undertake runoff forecasting, this study tried three cases (case 1: Climate Outlook + ESP, case 2: ESP probability through monthly measured discharge, case 3: Season ESP probability of case 2) according to techniques used to calculate ESP probabilities. As a result, the mean absolute error of runoff forecasts for case 1 proposed by this study was calculated as 295.8 MCM. This suggests that case 1 showed higher reliability in runoff forecasting than case 2 (324 MCM) and case 3 (473.1 MCM). In a discrepancy-ratio accuracy analysis, the Climate Outlook + ESP technique displayed 50.0%. This suggests that runoff forecasting using the Climate Outlook +ESP technique with the lowest absolute error was more reliable than other two cases.

• Journal of Korea Society of Waste Management
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• v.35 no.8
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• pp.777-784
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• 2018

Since climate change increases the risk of extreme rainfall events, concerns on flood management have also increased. In order to rapidly recover from flood damages and prevent secondary damages, fast collection and treatment of flood debris are necessary. Therefore, a quick and precise estimation of flood debris generation is a crucial procedure in disaster management. Despite the importance of debris estimation, methodologies have not been well established. Given the intrinsic heterogeneity of flood debris from local conditions, a regional-scale model can increase the accuracy of the estimation. The objectives of this study are 1) to identify significant damage variables to predict the flood debris generation, 2) to ascertain the difference in the coefficients, and 3) to evaluate the accuracy of the debris estimation model. The scope of this work is flood events in Ulsan city region during 2008-2016. According to the correlation test and multicollinearity test, the number of damaged buildings, area of damaged cropland, and length of damaged roads were derived as significant parameters. Key parameters seems to be strongly dependent on regional conditions and not only selected parameters but also coefficients in this study were different from those in previous studies. The debris estimation in this study has better accuracy than previous models in nationwide scale. It can be said that the development of a regional-scale flood debris estimation model will enhance the accuracy of the prediction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.341-346
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• 2022

Due to global warming and abnormal climate, the frequency and damage of floods are increasing, and the number of people exposed to flood-prone areas has increased by 25% compared to 2000. Floods cause huge financial and human losses, and in order to reduce the losses caused by floods, it is necessary to predict the flood in advance and decide to evacuate quickly. This paper proposes a flood risk determination model using a CNN-based classification model so that timely evacuation decisions can be made using rainfall and water level data, which are key data for flood prediction. By comparing the results of the CNN-based classification model proposed in this paper and the DNN-based classification model, it was confirmed that it showed better performance. Through this, it is considered that it can be used as an initial study to determine the risk of flooding, determine whether to evacuate, and make an evacuation decision at the optimal time.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.248-248
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• 2023

In recent years, the number of landslides in Korea has been increasing due to extreme weather events such as localized heavy rainfall and typhoons. Landslides often occur with debris flows, land subsidence, and earthquakes. They cause significant damage to life and property. 64% of Korea's land area is made up of mountains, the government wanted to predict landslides to reduce damage. In response, the Korea Forest Service has established a 'Landslide Information System' to predict the likelihood of landslides. This system selects a total of 13 landslide factors based on past landslide events. Using the LR technique (Logistic Regression) to predict the possibility of a landslide occurrence and the accuracy is known to be 0.75. However, most of the data used for learning in the current system is on landslides that occurred from 2005 to 2011, and it does not reflect recent typhoons or heavy rain. Therefore, in this study, we will apply a total of six machine learning techniques (KNN, LR, SVM, XGB, RF, GNB) to predict the occurrence of landslides based on the data of Inje, Gangwon-do, which was recently produced by the National Institute of Forest. To predict the occurrence of landslides, it is necessary to process converting landslide events and factors data into a suitable form for machine learning techniques through ArcGIS and Python. In addition, there is a large difference in the number of data between areas where landslides occurred or not. Therefore, the prediction was performed after correcting the unbalanced data using Tomek Links and Near Miss techniques. Moreover, to control unbalanced data, a model that reflects soil properties will use to remove absolute safe areas.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.62-62
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• 2023

강우가 지표면 아래로 침투할 때 초기에는 투수층이 불포화 상태이어서 부압이 작용하면서 침투할 것이다. Richards 식(Richards, 1931)을 써서 불포화 투수층의 침투를 모의할 수 있다. 강우가 지속되는 동안 하상 아래 어느 구간은 포화 상태가 되어 Richards 식을 더 이상 사용할 수 없다. 하지만 현재까지의 연구는 Richards 식을 사용하여 침투를 모의하는 오류를 범하고 있다. 강우에 의한 침투를 예측할 때 지표면에서의 침투율 q_b 가 필요한 데 현존하는 연구에서는 Horton 식(Horton, 1941)을 사용하여 초기 침투율 f_o 와 장시간 후 침투율 f_c 와 시간에 따라 지수함수로 감소하는 계수 k 의 3가지 계수값을 실험이나 현장 관측값에서 찾아서 쓰고 있다. 그런데, 이 계수값은 강우강도 r_i 가 클수록 침투율 q 가 커지는 물리 현상을 반영하지 못하는 한계가 있다. 본 연구에서 먼저 포화 투수층에서의 침투를 모의하는 식을 개발하였다. 지표면 아래에서 불포화 투수층에는 Richards식을 사용하고 포화 투수층에는 개발한 식을 사용하여 침투를 모의하였다. 또한 지표면에서의 침투율 q_b 를 구하는 공식을 개발하였다. 하상에서의 침투율의 최대값은 $q_{bmax}=-{\lambda}{\sqrt{2g(s-b)}}$ 일 것이다. 여기서 λ 는 투수층의 공극율, s 는 유출수면의 위치, b 는 지표면의 위치이다. 지표면에서의 침투율의 최소값 q_bmin 은 지표면 바로 아래 지점에서의 침투율일 것이다. 지표면에서의 침투율 q_b 로 q_bmax 와 q_bmin 사이의 적절한 값을 선택한다. 강우강도를 r_i 라고 하면 지표면 위 유출수의 연속방정식은 다음과 같다: $s-b={\int}(r_i-{\mid}q_b{\mid})dt$. 즉, 유출수면의 위치 s 는 강우강도 r_i 가 클수록 또는 지표면에서의 유출율의 크기 |q_b| 가 작을수록 크다. 또한 지표면에서의 침투율 q_b 와 지표면 아래에서의 침투율 q 는 s - b 가 클수록 크다. 따라서, 강우강도 r_i 가 클수록 침투율 q_b, q 가 큰 현상이 잘 반영되었다. 강우-침투-유출 모형실험을 수행하여 강우강도에 따라 침투율과 유출량이 다른 현상을 관측하여 수치실험 결과와 비교·검증하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.70-70
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• 2023

강우 발생 중 용담댐 상류로부터 용담댐으로 유입되는 유입량을 정확하게 예측하는 것은 하류 지역의 홍수 피해를 최소화하기 위한 댐의 적절한 운영에 필수적이다. 물리 기반 강우-유출 시뮬레이션 모형은 물리적 과정의 이해를 바탕으로 홍수 예측 분야에 광범위하게 사용되고 있다. 그러나 복잡한 물리 과정을 완벽히 이해하는 것은 거의 불가능하므로 다양한 가정 조건들을 이용해 복잡한 과정을 단순화하여 계산해야 하는 한계가 존재한다. 최근에는 방대한 데이터의 축적과 컴퓨터 능력의 향상으로 인해 데이터 기반 모형이 다양한 실무 문제를 해결하는 데 강력한 도구로 활용되고 있을 뿐 아니라 시뮬레이션 및 예측 등에도 다양하게 이용되고 있다. 그러나 예측 시간이 늘어날수록 입력자료로 이용되는 과거 자료와 출력자료로 이용되는 미래자료와의 상관관계가 줄어들어 모형의 성능이 저하된다. 따라서 본 연구에서는 용담댐의 시간당 유입량을 예측하기 위해 물리 기반 강우-유출 모형과 오차 보정 모형을 결합한 하이브리드 접근 방식을 제안한다. 물리 기반 강우-유출 모형으로는 HEC-HMS 모형을 사용하였으며, 오차 보정 모형에는 기계학습 모형인 인공신경망(Artificial Neural Network, ANN) 모형을 사용하였다. HEC-HMS 모형, ANN 및 하이브리드 모형(HEC-HMS + ANN)의 성능을 비교하기 위해 20 개의 홍수 사상을 모형 구축 및 검증에 사용하였다. 그 결과 하이브리드 모형은 예측 시간이 늘어날수록 HEC-HMS 및 ANN 모형보다 우수한 성능을 나타냈다. 물리모형에 기계학습을 이용한 오차 보정 절차를 통합한 경우 홍수 유출 예측의 정확성이 향상되었다. 다양한 모형의 비교 결과 본 연구에서 적용한 하이브리드 모형이 물리기반 강우-유출 모형 및 순수 기계학습 모형보다 우수한 성능을 보여줌으로써, 하이브리드 모형은 물리모형과 순수 기계학습 모형의 단점들을 보완하는데 이용할 수 있음을 나타낸다. 이 연구의 주요 목적은 강우-유출 시물레이션 모형의 오차 보정 기술에 대한 더 깊은 이해를 제공하는데 있다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.192-192
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• 2023

우량계는 강우 자료를 수집하는 전통적인 방법 중 하나로, 연속적이고 직접적인 설치가 가능하다. 하지만 지형적 특성에 영향을 받아 강우량을 과소 측정하는 문제점이 있다. 이러한 문제를 해결하기 위해 국지적인 호우, 강우 이동 및 강우 상황 등을 파악할 수 있는 레이더를 이용한 강우 측정이 활용된다. 하지만 레이더 기반 측정 또한 우량계와 마찬가지로 과소 측정하는 문제점이 있다. 측정 한계를 극복하기 위해 최근에는 위성 기반 강우 자료를 사용하고 있다. 위성 기반의 강우 자료는 측정이 어려운 장소에서도 강우량의 수집이 가능하며, 지표 변화를 관측하여 강우 측정의 정확도를 높일 수 있다. 고화질 위성 자료인 CHIRPS (Climate Hazards Group InfraRed Precipitation with Stations) 자료는 미국 국제개발처, 항공우주국, 해양 대기청의 지원으로 1980년부터 현재까지 전 지구적 (50°S-50°N, 180°E-180°W) 0.05° × 0.05°의 해상도를 가진 강우량 데이터를 개발하였다. 본 연구에서는 전국 54개 ASOS (Automated Synpotic Observing System)에서 관측한 월 단위 및 일 단위 강우 자료를 기준으로 CHIRPS 강우 자료를 비교하였다. 또한, 다른 위성 강우 자료들 (APHRODITE (Asian Precipitation Highly Resolved Observation Data Integration Towards Evaluation), CMORPH (Climate Prediction Cneter morphing method))과도 비교하여 국내 적용성을 확인하였다. 강우 자료의 정확도를 비교하기 위해서 Box-plot, RMSE (Root Mean Squared Error) 등을 산정하였으며, 강우 발생 일을 비교하고자 오차 행렬을 활용하였다. 비교 결과를 통해서 CHIRPS 강우 자료가 다른 위성 강우 자료들에 비해서 국내 적용성이 높은 것을 확인할 수 있었으며, 추후 국내 수문학 연구에서 기초자료로서 활용될 수 있을 것으로 판단된다.