유전 알고리즘을 이용한 다중스케일/다목적 공간계획 최적화모델 구축

학위논문 (박사)-- 서울대학교 대학원 : 환경대학원 협동과정 조경학전공, 2019. 2. 이동근.공간계획 과정에서 다양한 이해관계자와 결부된 목표와 제약 요건을 만족시키는 것은 복잡한 비선형적 문제로서 해결하기 어려운 것으로 알려져 왔다. 그러나 최근 이러한 문제에 유전 알고리즘 (genetic algorithms), 담금질 기법 (simulated annealing), 개미 군집 최적화 (ant colony optimization) 등의 다목적 최적화 알고리즘이 응용되고 있으며, 관련 연구 역시 급증하고 있다. 이 중 유전 알고리즘은 공간 최적화 부문에 가장 빈도 높게 적용된 최적화 알고리즘으로 exploration과 exploitation의 균형으로 합리적인 시간 내에 충분히 좋은 계획안을 제시할 수 있다. 그러나 공간 최적화 연구가 보여준 좋은 성과에도 불구하고 대부분의 연구가 특정 용도 혹은 시설의 배치에 집중되어 있으며, 기후변화 적응, 재해 관리, 그린인프라 계획과 같은 최근의 환경 이슈를 다룬 사례는 매우 미흡하다. 따라서 본 연구에서는 유전 알고리즘과 비지배 정렬 유전 알고리즘 (non-dominated sorting genetic algorithm II)에 기초하여 기후변화 적응, 재해 관리, 도시의 녹지 계획 등과 같은 환경 이슈를 공간계획에 반영할 수 있는 일련의 공간 최적화 모델을 제시하였다. 개별 환경 이슈에 따라 공간 해상도, 목적, 제약요건이 다르게 구성하였으며, 공간적 범위가 좁아지고 공간해상도는 높아지는 순서대로 나열하였다. 논문의 첫번째 장에서는 행정구역 도 규모 (province scale, 해상도 1㎢)에서 미래의 기후변화에 적응하기 위한 토지이용 시나리오를 모의할 수 있는 공간 최적화 모델을 제안하였다. 기후변화가 먼 미래가 아닌, 현재 이미 진행되고 있으며 관련한 다수의 피해가 관찰되고 있기 때문에 공간적 관점에서 기후변화에 대한 적응의 필요성이 지적되어 왔다. 그러나 구체적으로 기후에 대한 회복 탄력성을 향상시키기 위하여 토지이용의 공간적 구성을 어떻게 변화시켜야 할지에 대한 방법론 제시는 미흡하다. 지역계획에서 기후변화 영향을 고려한 토지이용 배분은 매우 유용한, 기본적인 중장기 적응 전략에 해당한다. 본 연구에서는 다목적 유전 알고리즘 (MOGA, multi-objective genetic algorithm)에 기초하여 9,982㎢에 350만의 인구가 거주하는 한국의 충청남도 및 대전광역시 일대를 대상으로 기후변화 적응을 위한 토지이용 시나리오를 제시하였다. 지역적인 기후변화 영향과 경제적 여건을 고려하여 재해 피해 및 전환량의 최소화, 벼 생산량, 종 풍부도 보전, 경제적 가치의 최대화 등 다섯 가지의 목적을 선택하였다. 각 목적 별 가중치를 변화시키며 여섯 가지 가중치 조합에 대한 17개의 파레토 최적 토지이용 시나리오를 생성하였다. 대부분의 시나리오는 정도의 차이는 있으나 현재의 토지이용에 비해 기후변화 적응 부분에서 더 좋은 퍼포먼스를 보였으므로, 기후변화에 대한 회복탄력성이 개선할 수 있을 것으로 판단하였다. 또한 공간 최적화 모델의 유연한 구조를 고려하였을 때, 지역의 실무자 역시 가중치와 같은 모델의 파라미터, 기후변화 영향 평가와 같은 입력자료를 변경함으로써 효율적으로 새로운 시나리오를 생성 및 선택하는 것이 가능할 것으로 예상하였다. 논문의 두 번째 장에서는 행정구역 군 규모 (local scale, 해상도 100m)에서 기후변화에 따른 재해 피해를 관리하기 위한 토지이용 시나리오를 모의할 수 있는 공간 최적화 모델을 제안하였다. 산악지형에서 폭우로 인한 산사태는 인명과 재산에 심각한 피해를 초래할 수 있는 것으로 알려져 있다. 더욱이 기후변화에 따른 강우의 변동성 증가로 이러한 산사태 빈도 및 강도 역시 증대될 것으로 예상된다. 일반적으로 산사태 리스크가 높은 지역을 피해 개발지역을 배치하는 것이 피해를 저감 혹은 회피할 수 있는 가장 효과적인 전략으로 알려져 있으나, 실제공간에서의 계획은 매우 복잡한 비선형의 문제로서 이것을 실현하는 데 어려움이 있다. 따라서 본 연구에서는 비지배 정렬 유전 알고리즘 II에 기초하여 산사태 리스크 및 전환량, 파편화의 최소화 등의 다양한 목적을 만족시키는 종합적인 토지이용 배분 계획을 제안하였다. 대상지는 2018년 동계올림픽 개최지인 한국의 평창군으로서 2006년에 산사태로 인한 대규모의 피해를 경험하였으나, 올림픽 특수 등의 개발압력으로 인한 난개발이 우려되는 지역이다. 최종적으로 한번의 모의를 통해 현재의 토지이용 보다 적어도 한가지 이상의 목적에서 좋은 퍼포먼스를 보이는 100개의 파레토 최적 계획안을 생성하였다. 또한 5개의 대표적인 계획안을 선정하여 산사태리스크 최소화와 전환량 최소화 간에 발생하는 상쇄 효과를 설명하였다. 본 연구결과는 기후변화와 관련된 공간 적응 전략의 수립, 보다 향상된 개발계획을 위한 의사결정을 효과적으로 지원할 수 있을 것으로 예상하였다. 논문의 세 번째 장에서는 블록 규모(neighborhood scale, 2m)에서 도시 내 녹지계획안을 모의할 수 있는 공간 최적화 모델을 제안하였다. 녹지 공간은 도시민의 삶의 질에 결정적인 영향을 미치기 때문에 다양한 도시 재생 및 개발계획에는 녹지와 직 간접적으로 관련된 전략이 포함된다. 녹지 공간은 도시지역 내에서 열섬 현상 완화, 유출량 저감, 생태 네트워크 증진 등 다양한 긍정적 효과가 있음이 알려져 있으나, 공간 계획의 관점에서 이러한 다양한 효과를 종합적, 정량적으로 고려된 사례는 매우 미흡하다. 따라서 본 연구에서는 비지배 정렬 유전 알고리즘 II에 기초하여 녹지의 생태적 연결성 증진, 열섬 효과 완화와 같은 다양한 효과와 설치에 따르는 비용을 종합적으로 고려하여 적절한 녹지의 유형과 위치를 결정한 녹지계획안을 제시하였다. 블록 규모의 가상의 대상지에 본 최적화 모델을 적용함으로써 30개의 파레토 최적 녹지계획안을 생성하였으며, 각 목적 간 퍼포먼스를 비교하여 녹지의 열섬 완화 효과와 생태적 연결성 증진 효과 간의 상승 관계 (synergistic relationship), 이러한 긍정적 효과와 비용 절감 간의 상쇄 효과 (trade-off relationship)를 분석하였다. 또한 다양한 계획안 중 대표적인 특성을 지니는 계획안, 다수의 계획안에서 공통적으로 녹지 설치를 위해 선택된 주요 후보지역 역시 규명하였다. 본 연구에서 제시된 모델은 계획안의 수정에서부터 정량적 평가, 계획안 선택에 이르는 일련의 긍정적인 피드백 과정을 수없이 반복함으로써 기존의 녹지계획 과정을 개선하는 데 기여할 수 있을 뿐만 아니라 모델의 결과 역시 다자간 협력적 디자인 (co-design)을 위한 초안으로서 활용될 수 있을 것으로 예상하였다.The meeting of heterogeneous goals while staying within the constraints of spatial planning is a nonlinear problem that cannot be solved by linear methodologies. Instead, this problem can be solved using multi-objective optimization algorithms such as genetic algorithms (GA), simulated annealing (SA), ant colony optimization (ACO), etc., and research related to this field has been increasing rapidly. GA, in particular, are the most frequently applied spatial optimization algorithms and are known to search for a good solution within a reasonable time period by maintaining a balance between exploration and exploitation. However, despite its good performance and applicability, it has not adequately addressed recent urgent issues such as climate change adaptation, disaster management, and green infrastructure planning. It is criticized for concentrating on only the allocation of specific land use such as urban and protected areas, or on the site selection of a specific facility. Therefore, in this study, a series of spatial optimizations are proposed to address recent urgent issues such as climate change, disaster management, and urban greening by supplementing quantitative assessment methodologies to the spatial planning process based on GA and Non-dominated Sorting Genetic Algorithm II (NSGA II). This optimization model needs to be understood as a tool for providing a draft plan that quantitatively meets the essential requirements so that the stakeholders can collaborate smoothly in the planning process. Three types of spatial planning optimization models are classified according to urgent issues. Spatial resolution, planning objectives, and constraints were also configured differently according to relevant issues. Each spatial planning optimization model was arranged in the order of increasing spatial resolution. In the first chapter, the optimization model was proposed to simulate land use scenarios to adapt to climate change on a provincial scale. As climate change is an ongoing phenomenon, many recent studies have focused on adaptation to climate change from a spatial perspective. However, little is known about how changing the spatial composition of land use could improve resilience to climate change. Consideration of climate change impacts when spatially allocating land use could be a useful and fundamental long-term adaptation strategy, particularly for regional planning. Here climate adaptation scenarios were identified on the basis of existing extents of three land use classes using Multi-objective Genetic Algorithms (MOGA) for a 9,982 km2 region with 3.5 million inhabitants in South Korea. Five objectives were selected for adaptation based on predicted climate change impacts and regional economic conditions: minimization of disaster damageand existing land use conversionmaximization of rice yieldprotection of high-species-richness areasand economic value. The 17 Pareto land use scenarios were generated by six weighted combinations of the adaptation objectives. Most scenarios, although varying in magnitude, showed better performance than the current spatial land use composition for all adaptation objectives, suggesting that some alteration of current land use patterns could increase overall climate resilience. Given the flexible structure of the optimization model, it is expected that regional stakeholders would efficiently generate other scenarios by adjusting the model parameters (weighting combinations) or replacing the input data (impact maps) and selecting a scenario depending on their preference or a number of problem-related factors. In the second chapter, the optimization model was proposed to simulate land use scenarios for managing disaster damage due to climate change on local scale. Extreme landslides triggered by rainfall in hilly regions frequently lead to serious damage, including casualties and property loss. The frequency of landslides may increase under climate change, because of the increased variability of precipitation. Developing urban areas outside landslide risk zones is the most effective method of reducing or preventing damageplanning in real life is, however, a complex and nonlinear problem. For such multi-objective problems, GA may be the most appropriate optimization tool. Therefore, comprehensive land use allocation plans were suggested using the NSGA II to overcome multi-objective problems, including the minimization of landslide risk, minimization of change, and maximization of compactness. The study area is Pyeongchang-gun, the host city of the 2018 Winter Olympics in Korea, where high development pressure has resulted in an urban sprawl into the hazard zone that experienced a large-scale landslide in 2006. We obtained 100 Pareto plans that are better than the actual land use data for at least one objective, with five plans that explain the trade-offs between meeting the first and the second objectives mentioned above. The results can be used by decision makers for better urban planning and for climate change-related spatial adaptation. In the third chapter, the optimization model was proposed to simulate urban greening plans on a neighborhood scale. Green space is fundamental to the good quality of life of residents, and therefore urban planning or improvement projects often include strategies directly or indirectly related to greening. Although green spaces generate positive effects such as cooling and reduction of rainwater runoff, and are an ecological corridor, few studies have examined the comprehensive multiple effects of greening in the urban planning context. To fill this gap in this fields literature, this study seeks to identify a planning model that determines the location and type of green cover based on its multiple effects (e.g., cooling and enhancement of ecological connectivity) and the implementation cost using NSGA II. The 30 Pareto-optimal plans were obtained by applying our model to a hypothetical landscape on a neighborhood scale. The results showed a synergistic relationship between cooling and enhancement of connectivity, as well as a trade-off relationship between greenery effects and implementation cost. It also defined critical lots for urban greening that are commonly selected in various plans. This model is expected to contribute to the improvement of existing planning processes by repeating the positive feedback loop: from plan modification to quantitative evaluation and selection of better plans. These optimal plans can also be considered as options for co-design by related stakeholders.1. INTRODUCTION 2. CHAPTER 1: Modelling Spatial Climate Change Land use Adaptation with Multi-Objective Genetic Algorithms to Improve Resilience for Rice Yield and Species Richness and to Mitigate Disaster Risk 2.1. Introduction 2.2. Study area 2.3. Methods 2.4. Results 2.5. Discussion 2.6. References 2.7. Supplemental material 3. CHAPTER 2: Multi-Objective Land-Use Allocation Considering Landslide Risk under Climate Change: Case Study in Pyeongchang-gun, Korea 3.1. Introduction 3.2. Material and Methods 3.3. Results 3.4. Discussion 3.5. Conclusion 3.6. References 4. CHAPTER 3: Multi-Objective Planning Model for Urban Greening based on Optimization Algorithms 3.1. Introduction 3.2. Methods 3.3. Results 3.4. Discussion 3.5. Conclusion 3.6. References 3.7. Appendix 5. CONCLUSION REFERENCESDocto

Creating Open Source Geodemographic Classifications for Higher Education Applications

This paper explores the use of geodemographic classifications to investigate the social, economic and spatial dimensions of participation in higher education. Education is a public service that confers very significant and tangible benefits upon receiving individuals: as such, we argue that understanding the geodemography of educational opportunity requires an application-specific classification, that exploits under-used educational data sources. We develop a classification for the UK higher education sector, and apply it to the Gospel Oak area of London. We discuss the wider merits of sector specific applications of geodemographics, with particular reference to issues of public service provision

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

SAT-CEP-monitor: An air quality monitoring software architecture combining complex event processing with satellite remote sensing

La contaminación del aire es un problema importante hoy en día que causa graves daños a la salud humana. Las áreas urbanas son las más afectadas por la degradación de la calidad del aire causada por las emisiones de gases antropogénicos. Aunque existen múltiples propuestas para el monitoreo de la calidad del aire, en la mayoría de los casos, se imponen dos limitaciones: la imposibilidad de procesar datos en tiempo casi real (NRT) para enfoques de teledetección y la imposibilidad de llegar a áreas de acceso limitado o baja cobertura de red para enfoques de datos terrestres. Proponemos una arquitectura de software que combina eficientemente el procesamiento de eventos complejos con datos de teledetección de varios sensores satelitales para monitorear la calidad del aire en NRT, brindando apoyo a los tomadores de decisiones. Ilustramos la solución propuesta calculando los niveles de calidad del aire para varias áreas de Marruecos y España, extrayendo y procesando información satelital en NRT. Este estudio también valida la calidad del aire medida por estaciones terrestres y datos de sensores satelitales.Air pollution is a major problem today that causes serious damage to human health. Urban areas are the most affected by the degradation of air quality caused by anthropogenic gas emissions. Although there are multiple proposals for air quality monitoring, in most cases, two limitations are imposed: the impossibility of processing data in Near Real-Time (NRT) for remote sensing approaches and the impossibility of reaching areas of limited accessibility or low network coverage for ground data approaches. We propose a software architecture that efficiently combines complex event processing with remote sensing data from various satellite sensors to monitor air quality in NRT, giving support to decision-makers. We illustrate the proposed solution by calculating the air quality levels for several areas of Morocco and Spain, extracting and processing satellite information in NRT. This study also validates the air quality measured by ground stations and satellite sensor data.This work was partially supported by the Spanish Ministry of Science and Innovation and the European Regional Development Fund (ERDF) under project FAME [RTI2018-093608-B-C33]. The corresponding author thanks the ERASMUS+ KA107 program for the grant and acknowledges the University of Cadiz for the academic supervision and their research facilities, grant number: 2017-1-ES01- KA107-037422 and 2018-1-ES01-KA107-049705. The authors of this work are also thankful to the Andalusian and Madrid regional governments for providing us with the NRT MGS data

Forecasting Unemployment Rate Using a Neural Network with Fuzzy Inference System

Greece is a low-productivity economy with an ineffective welfare state, relying almost exclusively on low wages and social transfers. Failure to come to terms with this reality hampers both the appropriateness of EU recommendations and the Greek government's capacity to deal with unemployment. Rather than finding a job in a family business or through relationship contacts, young people stay unemployed. Nor can people move back to their village of origin so easily. The underground economy, and the mass of small companies which characterize the Greek economy are booming, on paper. One in three members of the workforce are "self-employed", compared to one in seven in the EU as a whole. (International Viewpoint) An unemployed person in Greece is 2,15 times more likely to suffer poverty than a person in employment. Yet in Greece there are perhaps even more influential factors in determining increased risk of poverty. Thus while unemployment is a crucial factor in the risk of poverty, it is neither the only nor the most significant factor. The paper presents a new technique in the field of unemployment modeling in order to forecast unemployment index. Techniques from the Artificial Neural Networks and from fuzzy logic have been combined to generate a neuro-fuzzy model. The input is a time series. Classical statistics measures are calculated in order to asses the model performance. Further the results are compared with an ARMA and an AR model.forecasting, neural network, unemployment

A decision support system for integrated semi-centralised urban wastewater treatment systems

The importance of adequate water supply and sanitation infrastructure as cornerstones for the development of civilizations is undeniable. Although a strategy based on centralised infrastructure has proven to be successful in the past, in some circumstances such conventional systems are inappropriate for future needs. A Semi-centralised Urban Wastewater Treatment System (SUWWTS) may be considered a viable sustainable urban water management solution to promote water security. A SUWWTS merges regulations of traditional centralised systems with the concepts of close-loop and resource recovery of decentralised systems. However, research on the design and feasibility of implementing semi-centralised systems is in its infancy. This Thesis is a first attempt to articulate the complexity, to systematize and to automatize the design of a SUWWTS. Here we show a novel method, referred to as framework, for the development of SUWWTS with allowance for the socio-economic and geographic context of any urban area. To demonstrate the proposed framework a Decision Support System (DSS) was developed; its output is a recommended design comprised of several wastewater treatment plants, their respective technology, and their associated sewerage and reclaimed water distribution networks. The results demonstrate the capabilities and the usefulness of the DSS; it applies the design engineers’ subjective preferences, such as regional technological inclinations and implementation strategies. The results from a feasibility study on the city of Rio de Janeiro validated and demonstrated how the DSS can be used to assist decision-makers. This Thesis discusses the framework, the DSS and the demonstration case. Overall, it will hopefully help both other researchers and practitioners by contributing to the discussion on how to promote urban water security, to decrease urban areas’ dependency on ecosystem services whilst delivering better social welfare

Power quality and electromagnetic compatibility: special report, session 2

The scope of Session 2 (S2) has been defined as follows by the Session Advisory Group and the Technical Committee: Power Quality (PQ), with the more general concept of electromagnetic compatibility (EMC) and with some related safety problems in electricity distribution systems. Special focus is put on voltage continuity (supply reliability, problem of outages) and voltage quality (voltage level, flicker, unbalance, harmonics). This session will also look at electromagnetic compatibility (mains frequency to 150 kHz), electromagnetic interferences and electric and magnetic fields issues. Also addressed in this session are electrical safety and immunity concerns (lightning issues, step, touch and transferred voltages). The aim of this special report is to present a synthesis of the present concerns in PQ&EMC, based on all selected papers of session 2 and related papers from other sessions, (152 papers in total). The report is divided in the following 4 blocks: Block 1: Electric and Magnetic Fields, EMC, Earthing systems Block 2: Harmonics Block 3: Voltage Variation Block 4: Power Quality Monitoring Two Round Tables will be organised: - Power quality and EMC in the Future Grid (CIGRE/CIRED WG C4.24, RT 13) - Reliability Benchmarking - why we should do it? What should be done in future? (RT 15

Modeling Land-Cover Types Using Multiple Endmember Spectral Mixture Analysis in a Desert City

Spectral mixture analysis is probably the most commonly used approach among sub-pixel analysis techniques. This method models pixel spectra as a linear combination of spectral signatures from two or more ground components. However, spectral mixture analysis does not account for the absence of one of the surface features or spectral variation within pure materials since it utilizes an invariable set of surface features. Multiple endmember spectral mixture analysis (MESMA), which addresses these issues by allowing endmembers to vary on a per pixel basis, was employed in this study to model Landsat ETM+ reflectance in the Phoenix metropolitan area. Image endmember spectra of vegetation, soils, and impervious surfaces were collected with the use of a fine resolution Quickbird image and the pixel purity index. This study employed 204 (=3x17x4) total four-endmember models for the urban subset and 96 (=6x6x2x4) total five-endmember models for the non-urban subset to identify fractions of soil, impervious surface, vegetation, and shade. The Pearson correlation between the fraction outputs from MESMA and reference data from Quickbird 60 cm resolution data for soil, impervious, and vegetation were 0.8030, 0.8632, and 0.8496 respectively. Results from this study suggest that the MESMA approach is effective in mapping urban land covers in desert cities at sub- pixel level.