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홍수 방어 인프라의 회복탄력성 평가

기후변화의 영향으로 인해 빈번해지는 홍수 발생빈도와 그로 인한 자연재해 증가로 발생하는 도시 인적, 물적 피해의 규모가 상승하고 있습니다. 도시 홍수를 방어하고 피해를 최소하하기 위해 홍수방어를 위한 사회기반시설을 설치하여 운영하고 있으나 과거 강우 및 홍수 관련 데이터를 활용한 사후조치 위주로 운영이 되어 향후 예측하기 어려운 기후변화에 대응하기 어렵습니다. EECOS에서는 기후변화에 의한 홍수발생에 선제적이고 능동적인 대응방안을 마련하기 위해 사회기반시설에 대한 예방, 흡수, 복구, 적응 및 전환 등의 '회복탄력성' 개념 도입을 위한 기반을 마련하고자 합니다. '외부교란에 대한 시스템의 성능 유지/회복 능력' 을 뜻하는 회복탄력성 개념을 사회기반시설에 도입하기 위하여 복잡하고 다양한 요소들로 구성된 회복탄력성 평가 요소를 구체화하여 향후 기후변화에 의한 극한 홍수사상에 대한 홍수 방어를 위한 평가 및 관리체계 구축을 위한 연구를 진행하고 있습니다.

세부 연구내용

- 홍수방어 인프라의 피해 발생 요인과 피해 사례 조사
- 홍수 발생에 의한 홍수방어 인프라의 회복탄력적 방재거동 분석
- 홍수방어 인프라의 회복탄력성 평가지표 개발
- 기후변화 대응 홍수방어 인프라 회복탄력성 예측 및 제도개선 방안 도출

Resilience of Ecological Network in Wetlandscape

Wetlands distributed in a large landscape play a critical role in providing various ecosystem services including the provision of ecological habitats, hydrologic controls, and biogeochemical processes. These services are, however, also controlled by hydro-climatic and geological conditions and dispersal pattern of inhabiting species.  We are interested in various dispersal models to allow dispersal strategies between habitats. Implications of modeling ecological networks will provide a new decision-making process, especially for conservation purposes.

Generating ecological networks using dispersal models (Left: threshold; middle: exponential kernel; right: heavy-tailed model)

Topological analysis of urban water network

For the provision of a reliable supply of water services, water distribution network should be designed and managed to cope with various threats (e.g., disasters). Due to physical properties of this kind of infrastructure, we can view it as a network (or graph) to apply complex system network theory. However, a primal network has limitations to analyze network topology because of spatial features of the network. We aim to develop a dual method to enable getting a deeper insight and more meaningful analytical results from this network. This newly obtained information will be helpful for improving the resilience of infrastructures as complex networks.

손재우 연구_거제도상수도관망.jpg

Fig 1. Analysis of water distribution network topology, Geojedo Island, South Korea.

손재우 연구_전주상수도관망.jpg

Fig 2. Water distribution system, Jeon-ju city, South Korea.

Water Cycle Sustainability in Megacities

Many cities are facing various water-related challenges caused by rapid urbanization and climate change. Moreover, a megacity may pose a greater risk due to its scale and complexity for coping with impending challenges. Thus, it is important to diagnose key barriers and opportunities in a city's environmetal surroundings, infrastructures, and governance regarding water management to enable building a resilient urban society. By adopting the City Blueprint® Approach, we try to assess the various aspects that govern the water cycle of a city. Based on the assessment, we also try to propose priority of specific strategies that should be applied for improving urban water resilience.

City Blueprint Approach


Topology and Resilience of Socio-technical Networks

We are interested in topology and its relation to the resilience of various networked infrastructures such as power grids. As a case study, we have analyzed Korean power grid (KPG) providing another empirical evidence of power grid topology. We identify node degree distribution, efficiency and clustering coefficient, etc. We also do the analysis to test error and attack tolerance of the networks using various scenarios (e.g., intentional vs. random attacks, cascading failures).

For more details, see our recent publication in Physica A

In addition to viewing infrastructure as a solely technicial network, we view it as an engineered complex system coupled by social and technical system. The logic underlying this is by recognizing how well a system recovers from failures depends on policies and protocols for human and organizational coordination that must be considered alongside technological analyses.

Here is our another recent publication in COMPEXITY.

Map of Korean power grid

(Source:  Eisenberg et al. 2018)

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