• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.642-642
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• 2012

최근에 들어 지구온난화에 따른 기후변화의 영향으로 국지성 집중호우와 돌발성 호우가 한반도 뿐 아니라 전 세계적으로도 많이 나타나고 있고, 그로 인한 이상홍수의 발생이 우리나라의 인명 및 재산피해를 날로 증가시키고 있는 추세이다. 그러나 현재 국내의 홍수방어시스템은 정확도 및 선행시간 확보 등의 측면에서 국민들의 요구수준까지는 그 역할을 수행하지 못하고 있는 실정이다. 또한 최근 4대강 살리기 사업을 통해 수행된 보 설치 및 하도 준설로 인해 하천환경의 변화가 크게 발생하여, 보다 정확하고 신속한 홍수위 예측기법이 요구되고 있는 실정이다. 이에 따라 현재 4대강 홍수통제소에서는 정확한 홍수위예측을 위해 4대강 본류 및 주요 지류에 대해 수리모형을 구축하고 있고, 기존의 저류함수모형에 의한 강우-유출 해석기법을 적용하여 주요 지류에 대한 유입량을 산정하기 위한 모형을 구축중에 있다. 국내 홍수방어 시스템에 현재까지 사용되어 오고 있는 저류함수모형 및 수위-유량 관계식을 이용한 방법은 물리적 기반의 홍수예측모형으로 유역의 지형학적 인자와 그에 따른 여러 변수를 포함하기 때문에 하천환경의 변화로 인해 각각의 추적과정에서 오차들이 발생하여 해석결과에 영향을 미치는 단점이 있다. 이에 반해 데이터 기반 모형은 강우-유출 모형에서 사용되는 많은 수문학적 자료 및 매개변수들의 사용 없이 오직 수위 및 강우측정 자료만을 이용하여 홍수를 예측하는 모형이다. 본 연구에서는 낙동강 유역에 대해 보다 정확한 홍수위 예측을 위해 현재 낙동강홍수통제소에서 구축중인 낙동강 본류의 수리모형의 주요 지류의 유입량 산정을 위해 기존의 물리적 기반 모형이 아닌 뉴로-퍼지(Neuro-Fuzzy) 모형을 이용한 data 기반 모형을 적용해 기존 물리적 기반 모형과 비교 분석 하고자 하였다. 낙동강의 주요지류인 감천, 금호강, 남강, 내성천, 밀양강, 반변천, 위천, 황강을 적용유역으로 선정하여 유역별로 티센망을 구축하였고, 각 지류별로 수위관측소를 선정하여 최근 10년동안 낙동강유역의 홍수예 경보가 발령되었거나 많은 비가 온 사상을 선정해 모형을 검증하였다. 모형은 실시간 수위측정 자료와 강우자료 및 해당유역 댐의 방류량 자료를 이용해 유역별 최적 입력자료 조합을 선정하여 간단하게 구축할 수 있었다. 또한 10분 단위 및 30분 단위의 입출력 자료로 모형을 구축하여 비교하였다. 이번 연구에서 수행한 낙동강유역에서의 뉴로-퍼지(Neuro-Fuzzy) 모형을 이용한 홍수예측기법을 통해 몇가지 data만으로 유역의 주요지점에 대한 홍수위와 홍수량을 예측할 수 있음을 확인할 수 있었다. 모의 결과는 실측치와 비교해 정확도 면에서 우수함을 보여 주었으나 예측시간이 길어질수록 실측치의 경향을 벗어나는 결과를 보였다. 그러나 실시간 홍수예 경보에 있어서는 만족할만한 선행시간을 확보할 수 있었다. 구축된 Data 기반 모형이 물리적 기반 모형과 더불어 낙동강 홍수예 경보를 위한 모형으로 사용될 수 있다면 보다 효율적인 예 경보 체계 구축에 도움을 줄 수 있을 것으로 판단된다.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1761-1775
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• 2022

Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.

• Journal of Korea Water Resources Association
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• v.49 no.7
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• pp.589-597
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• 2016

In this study, we investigated the use of satellite-derived flow (SDF) signals and a data-based model for the estimation of outflow for the river reach where in situ measurements are either completely unavailable or are difficult to access for hydraulic and hydrology analysis such as the upper basin of Imjin River. It has been demonstrated by many studies that the SDF signals can be used as the river width estimates and the correlation between SDF signals and river width is related to the shape of cross sections. To extract the nonlinear relationship between SDF signals and river outflow, Artificial Neural Network (ANN) model with SDF signals as its inputs were applied for the computation of flow discharge at Imjin Bridge located in Imjin River. 15 pixels were considered to extract SDF signals and Partial Mutual Information (PMI) algorithm was applied to identify the most relevant input variables among 150 candidate SDF signals (including 0~10 day lagged observations). The estimated discharges by ANN model were compared with the measured ones at Imjin Bridge gauging station and correlation coefficients of the training and validation were 0.86 and 0.72, respectively. It was found that if the 1 day previous discharge at Imjin bridge is considered as an input variable for ANN model, the correlation coefficients were improved to 0.90 and 0.83, respectively. Based on the results in this study, SDF signals along with some local measured data can play an useful role in river flow estimation and especially in flood forecasting for data-scarce regions as it can simulate the peak discharge and peak time of flood events with satisfactory accuracy.

• Journal of Korean Society of Disaster and Security
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• v.15 no.1
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• pp.1-12
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• 2022

An automatic optimization technique is used to estimate the optimal parameters of the hydrologic model, and different hydrologic response results can be provided depending on objective functions. In this study, the parameters of the event-based rainfall-runoff model were estimated using various objective functions, the reproducibility of the hydrograph according to the objective functions was evaluated, and appropriate objective functions were proposed. As the rainfall-runoff model, the storage function model(SFM), which is a lumped hydrologic model used for runoff simulation in the current Korean flood forecasting system, was selected. In order to evaluate the reproducibility of the hydrograph for each objective function, 9 rainfall events were selected for the Cheoncheon basin, which is the upstream basin of Yongdam Dam, and widely-used 7 objective functions were selected for parameter estimation of the SFM for each rainfall event. Then, the reproducibility of the simulated hydrograph using the optimal parameter sets based on the different objective functions was analyzed. As a result, RMSE, NSE, and RSR, which include the error square term in the objective function, showed the highest accuracy for all rainfall events except for Event 7. In addition, in the case of PBIAS and VE, which include an error term compared to the observed flow, it also showed relatively stable reproducibility of the hydrograph. However, in the case of MIA, which adjusts parameters sensitive to high flow and low flow simultaneously, the hydrograph reproducibility performance was found to be very low.