최적 용도지역 배치를 위한 GA 시뮬레이션

다국어 초록 (Multilingual Abstract)

In urban planning, land use arrangement problem is essential and important. Until now, land use arrangement depends on a planner's rational thought process. The planner tries to find better land use pattern in a given situation by many sided analysis, comparison and examination. However, as the value considered in land use arrangement get more and the correlation among values gets more complex, it is not easy to find the best land use pattern.
The development of computer systems and various analysis techniques using it help solve complex urban problems. Genetic algorithm(GA), one of the techniques of artificial intelligence modeling the phenomenon of inheritance and selection, is being studied in the areas of architecture and urban planning. GA has several advantages. Most of all, it is an analysis technique most prominent in finding Global Optimum.
This study aims at constructing a model of land use arrangement using GA and developing a PSS(planning support system) capable of simulating according to the variation of conditions.
First, urban regions were divided by grid in order to apply GA to arrangement problem and chromosome code method was studied. This study try to the capability of applying GA to land use arrangement, selecting both location appropriateness and combination considering adjacency of cells as a variable. GA model constructed Delphi 5.0 language. Second, constructed GA model was applied to case areas which are Bang-eo dong, Hwa-jeong dong and Il-san dong in Ulsan. Data construction and simulation result was visualized by linking ArcView GIS of Esri Co.
The simulation is an association of each parameter, case 1 considering location appropriateness only, case 2 combination only, case 3 both location appropriateness and combination. The values of GA parameters were first population size 100, 200, 300, crossover number point 1, 2, 3 and mutation rate 0.01, 0.03, 0.05 and the simulation was carried out with the association of values of each parameter. The simulation result verified how well land use arrangement was reappeared, which is randomly arranged now.
According to the simulation result, GA model constructed through this thesis reappeared present land use pattern, which was randomly arranged, with accuracy of case 1 0.9, case 3 0.95 and more. And Users were free to set up target areas, the values of each parameter and GA parameter in interface constructed in Delphi 5.0. And the simulation result was visualized by linking ArcView GIS of Esri Co. PSS for land use arrangement which has been developed through this thesis was proved to carry out simulation effectively about various scenarios.

목차 (Table of Contents)

• 목차
• Abstract
• I. 서론
• 1. 연구의 배경 및 목적 = 1
• 2. 연구의 범위 및 방법 = 2
• 1) 연구의 범위 = 2
• 2) 연구의 방법 = 3
• 3. 관련 연구 동향 = 4
• II. GA의 기본 이론 및 사례 연구
• 1. GA의 개요 = 7
• 2. 인공지능의 분류 = 7
• 3. GA의 이론적 원리 = 9
• 1) GA의 용어 해석 = 9
• 2) GA의 기본적인 구조 = 10
• 3) 염색체의 부호화(Encoding) = 11
• (1) Binary Encoding = 11
• (2) Permutation Encoding = 12
• (3) Value Encoding = 12
• 4) 선택·도태(Selection) = 13
• (1) Roulette Wheel Selection = 14
• (2) Elitist Preserving Selection = 14
• (3) Rank Selection = 14
• 5) 교차(Crossover) = 15
• (1) 단순교차(simple crossover) = 15
• (2) 복수점 교차(multipoint crossover) = 16
• (3) 일정교차(uniform crossover) = 16
• 6) 돌연변이(Mutation) = 17
• 7) 스키마타 이론(Schemata Theory) = 18
• III. 용도지역에 관한 GA 모델링
• 1. 용도지역 배치를 위한 기본 설정 = 20
• 2. 염색체의 코드화 = 20
• 3. 평가함수의 설정 = 21
• 4. 유전자 조작 = 23
• 1) 교차(Crossover) = 23
• 2) 돌연변이(Mutation) = 23
• IV. 시뮬레이션
• 1. 사례지역의 현황 = 25
• 2. 사례지역의 데이터 구축 = 26
• 1) 사례지역의 자연 지리적 특징 현황 = 26
• 2) 용도지역 현황 = 27
• 3. 평가함수의 가중치 설정 = 28
• 4. 시뮬레이션 과정 = 30
• 5. 시뮬레이션 결과 = 31
• 1) case1(자연 지리적 입지 적합성만을 고려한 경우) = 31
• 2) case2(상호결합력만을 고려한 경우) = 36
• 3) case3(자연 지리적 입지 적합성과 상호 결합력 동시 고려) = 38
• V. 용도지역 배치를 위한 계획지원시스템(PSS)
• 1. 시스템의 구조 = 44
• 2. 시스템의 구성 = 46
• 1) 대상지의 선택 = 46
• 2) 각 변수의 가중치 설정 = 47
• 3) GA 파라미터 값의 설정 = 48
• 4) 시뮬레이션 결과의 시각화 = 48
• VI. 결론 및 고찰
• 참고문헌 = 53