낙동강 유역의 실시간 홍수예측 시스템 구축 = Development of Real-time Flood Forecasting System in Nakdong River Basin

다국어 초록 (Multilingual Abstract)

Recently, the damage on people and properties are rapidly increasing by flood disaster such as inundation due to typhoons and localized heavy rain affected by abnormal climate. In domestic cases, large-scaled flood damage habitually occurred by seasonal concentrated phenomena of precipitation due to typhoon lusa in 2002 and typhoon maemi in 2003. To reduce the realistic flood damage, it is urgently necessary to establish the non-structural measures that develops flood forecasting system that can forecast the possible flood in the future. Currently, a physical-based precipitation-discharge model that is used on working-level has large or small errors in the process of calculating the discharge amount from hydrological data.
The purpose of this study is to develop an efficient flood forecasting system by real-time forecasting using Neuro-Fuzzy model and regression model which is a data-based model to complement problems that the physical-based precipitation-discharge model. Through this, by forecasting real-time flood risk that occurs by localized heavy rain, a system can be developed to make a safe society from flood disaster by protecting lives and properties of residents within the region.
The Neuro-Fuzzy model started to be applied as a trial to make the best use of the advantages of a neuro network and fuzzy theory, and to complement the disadvantages that each technique has. Both the neuro network theory and fuzzy theory are interested in establishing a system that can work as a human for specific fields, but the fields in which the two theories has strong points are slightly different. The fuzzy theory is proper for processing and inferring ambiguous data under the logical base and is useful for highly difficult processing through natural linguistic expression. The neuro network theory has learning ability so there is a large flexibility in composition of a system, and it is excellent in data-based processing.
Various combinations of input data were constructed by using rainfall and water level and discharge 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 Nakdong river basin. Neuro-Fuzzy system was constructed by estimating the number and parameters of membership function for each leadtime of selected training and checking events.
This study composed and applied a combination of various conditions of input data on precipitation, water level and discharge to Nam River basin for selecting the optimum one for Neuro-fuzzy system and for real-time flood forecasting. Time scale for input data in a multiple linear regression model was determined in consideration of the input data selected this way. Besides, excluding the data of extensive amounts demanded by the existing rainfall-runoff model, on the subject of 8 major tributaries of Nakdong River basin, Neuro-fuzzy model and regression analysis model, which can predict the flood level and flood flow on major points of the basin with only data of precipitation, water level and discharge, were constructed and applied to the subject basin to forecast the flood level and flow by station.
Applying the boundary condition of forecasted floods from tributary, river flood level in the mainstream was predicted by one-dimensional model, which was reviewed comparatively with the simulation result using the actual flood. The result of simulation results showed that forecasting of flow rate at the points after flowing the mainstream by tributaries well agreed with the survey values and the prediction of the highest water level for the whole mainstream of the Nakdong River was in accord with the result of simulation with a boundary condition of actual flood of observation. Through this, real-time river interpretation using Neuro-fuzzy flood prediction is considered to be used in the prediction of floods for the section of the Nakdong River basin.

목차 (Table of Contents)

• 제 1 장 서 론 1
• 1.1 연구목적 2
• 1.2 연구동향 3
• 1.3 연구내용 7
• 제 2 장 실시간 홍수예측 시스템 구축 10
• 2.1 실시간 홍수예측 10
• 2.1.1 실시간 홍수예측의 필요성 10
• 2.1.2 실시간 홍수예측 시스템 구축 방법 12
• 2.2 뉴로-퍼지 모형 15
• 2.2.1 뉴로-퍼지의 개요 15
• 2.2.2 뉴로-퍼지 모형의 구조 16
• 2.2.3 퍼지 군집화 22
• 2.3 다중선형회귀 모형 26
• 2.3.1 회귀분석의 개요 26
• 2.3.2 회귀계수 추정기법 27
• 2.3.3 단계별 선택법 31
• 2.4 1차원 수리해석 모형 34
• 제 3 장 모형의 입력자료 조합 및 선정 43
• 3.1 대상유역의 개황 44
• 3.2 입력자료 조합의 구성 48
• 3.3 뉴로-퍼지 최적 입력자료 조합 선정 52
• 제 4 장 낙동강 유역에 대한 적용 64
• 4.1 실시간 홍수예측 64
• 4.1.1 뉴로-퍼지 모형 70
• 4.1.2 다중선형회귀 모형 72
• 4.1.3 실시간 홍수예측 결과 및 비교 81
• 4.2 실시간 하도추적 101
• 4.2.1 경계조건 선정을 위한 실시간 홍수 예측 101
• 4.2.2 실시간 하천해석 결과 및 비교 105
• 제 5 장 비교 및 검토 111
• 제 6 장 결 론 114
• 참 고 문 헌 117