중소하천에서의 실시간 홍수예측 및 범람해석 = Real-time Flood Forecasting and Inundation Analysis in Medium and Small Streams

다국어 초록 (Multilingual Abstract)

Since the frequency and scale of damage from the torrential rain increases recently due to climate change and global warming, the significance of flood forecasting and warning becomes important in medium and small streams as well as large rivers. Real time flood forecasting is important in a flood-prone region within medium and small sized watershed, for the issue of flood warnings in order to allow ample leadtime for the evacuation of populations and protection of primary facilities endangered by imminent rising water levels.
In the modeling the relations with a rainfall and runoff by using the existing hydrological model, it implies lots of non-linear and uncertain elements. Through watershed flood routing, reservoir routing and channel routing for the estimation of runoff, many diverse errors can be occurred and accumulated, so that the results of rainfall-runoff analysis may contain the uncertainties in the existing flood forecasting and warning system.
The purpose of this study is develop the data driven model for the real-time flood forecasting and inundation analysis that are improved the problems of the existing hydrological model. Neuro-Fuzzy model which use the fuzzy inference theory with neural network, that can forecast flood only by using the rainfall and flood level and discharge data without using lots of physical data that are necessary in existing hydrological rainfall-runoff model is used.
First of all, the various combinations of input data were constructed by using rainfall and water level to select optimal combinations of input data for applying Neuro-Fuzzy model. The optimal combination of input data that composed of rainfall data at t time, rainfall data at t+1 time, water level data at t time, water level data at t+1 time, water level data at t+2 time was selected when current time is t time. Based on optimal combination of input data, training and checking events are selected by using various flood events for applying Neuro-Fuzzy model in Jungrangcheon. Neuro-Fuzzy system was constructed by estimating the number and parameters of membership function for each leadtime of selected training and checking events. In addition, DEM based two-dimensional inundation model that can perform effectively real-time inundation analysis in protected lowland has been developed.
The suggested Neuro-Fuzzy method was applied to Jungrangcheon. For the verification of the model, the comparisons between forecasting floods and observation data are presented. The comparison results of Neuro-Fuzzy model in Jungrangcheon with the observed data show that the real-time flood forecasting can be simulated successfully without large errors. In case of the 180 minutes leadtime, a root mean square error turned out to be 0.174m, the correlation coefficient to be 0.887 and in other statistical indicators such as mean absolute deviation and Nash-Sutcliffe efficient coefficient, forecasting results are much improved in terms of flood level and discharge. Accordingly, three hours leadtime with high accuracy can be obtained for real-time flood forecasting.
The forecasted floods by applying Neuro-Fuzzy model are used as boundary conditions to simulate the real-time one-dimensional hydraulic analysis for jungrangcheon. As a result of verification, the simulated results by using forecasted floods are in good agreements with the simulated results which use actual observation floods as boundary conditions.
In order to estimate for calibration and verification of real-time two-dimensional inundation forecasting model, DEM based inundation model and level-pool model developed in this study are applied to a flood event in 1998 year in Jungrangcheon. The results of two models were compared with the actually investigated inundation area. From the comparisons , it revealed that results of DEM-based model are better accurate and improved than those of level-pool model. The computational time for each used grid size is reviewed in terms of real-time inundation forecasting. As a result, the computational time of DEM-based inundation model used 20m×20m DEM data was only 125 seconds. Therefore DEM-based inundation model developed in this study will be able to obtain sufficient leadtime with high accuracy for real-time inundation forecasting within 3 minutes.
The methodologies presented in this study provide an advanced integrated system of real-time flood forecasting and inundation analysis which can be applicable for flood prevention and mitigation in medium and small streams. This study can greatly contribute to the construction of a high accuracy flood information system that secure leadtime in medium and small streams.

목차 (Table of Contents)

• 제 1 장 서 론 1
• 1.1 연구목적 3
• 1.2 연구동향 5
• 1.3 연구내용 13
• 제 2 장 실시간 홍수예측 시스템 구축 17
• 2.1 중소하천에서의 실시간 홍수예측 17
• 2.1.1 실시간 홍수예측의 필요성 및 중요성 17
• 2.1.2 기존 홍수예측의 한계 18
• 2.1.3 실시간 홍수예측 및 범람 시스템 구축 방법 20
• 2.2 신경망 이론 24
• 2.2.1 구성요소 25
• 2.2.2 학습규칙 29
• 2.2.3 오류 역전파 알고리듬 32
• 2.3 퍼지이론 34
• 2.3.1 퍼지이론의 개요 35
• 2.3.2 퍼지 집합(Fuzzy Set) 36
• 2.3.3 소속 함수 39
• 2.3.4 퍼지추론 41
• 2.3.5 비퍼지화(defuzzification) 44
• 2.4 뉴로-퍼지 모형 46
• 2.4.1 뉴로-퍼지의 개요 46
• 2.4.2 뉴로-퍼지 모형의 구조 47
• 2.4.3 뉴로-퍼지 학습 알고리즘 52
• 2.4.4 퍼지 군집화(Fuzzy Clustering) 53
• 2.5 1차원 하천홍수 해석 57
• 2.6 2차원 범람해석 65
• 2.6.1 Level-pool 범람모형 66
• 2.6.2 DEM기반 범람모형 68
• 제 3 장 뉴로-퍼지 최적 입력자료 조합 79
• 3.1 대상유역의 개황 80
• 3.2 입력자료 조합의 구성 82
• 3.3 최적 입력자료 조합 선정과정 87
• 3.4 최적 입력자료 조합 선정결과 89
• 제 4 장 중랑천 유역에 대한 적용 109
• 4.1 실시간 홍수예측 109
• 4.1.1 유역의 개황 및 호우사상의 선정 109
• 4.1.2 뉴로-퍼지 모형구축 112
• 4.1.3 실시간 홍수예측 결과 121
• 4.2 실시간 하천해석 137
• 4.2.1 경계조건 선정을 위한 실시간 홍수 예측 137
• 4.2.2 실시간 하천해석 결과 145
• 4.3 실시간 2차원 범람해석 160
• 4.3.1 대상유역 160
• 4.3.2 붕괴유량 산정 163
• 4.3.3 지형자료의 구축 165
• 4.3.4 실시간 2차원 범람해석 결과 168
• 제 5 장 비교 및 고찰 175
• 5.1 실시간 홍수예측 175
• 5.2 실시간 하천해석 180
• 5.3 실시간 범람해석 184
• 5.3.1 최대 침수심과 침수 면적에 대한 비교 184
• 5.3.2 침수실적도에 대한 적합도 비교 186
• 5.3.3 계산소요 시간에 대한 비교 188
• 제 6 장 결 론 192
• 참 고 문 헌 196