Vulnerability analysis in Complex Networks under a Flood Risk Reduction point of view
Leonardo B. L. Santos, Giovanni G. Soares, Tanishq Garg, Aurelienne A. S. Jorge, Luciana R. Londe, Regina T. Reani, Roberta B. Bacelar, Carlos E. S. Oliveira, V… 展开作者
Leonardo B. L. Santos, Giovanni G. Soares, Tanishq Garg, Aurelienne A. S. Jorge, Luciana R. Londe, Regina T. Reani, Roberta B. Bacelar, Carlos E. S. Oliveira, Vander L. S. Freitas, Igor M. Sokolov
The measurement and mapping of transportation network vulnerability to natural hazards constitute subjects of global interest, especially due to climate change, and for a sustainable development agenda. During a flood, some elements of a transportation network can be affected, causing loss of life of people and damage to vehicles, streets/roads, and other logistics services, sometimes with severe economic impacts. The Network Science approach may offer a valuable perspective considering one type of vulnerability related to network type critical infrastructures: the topological vulnerability. The topological vulnerability index associated with an element is defined as the reduction in the network's average efficiency due to the removal of the set of edges related to that element. We present a topological vulnerability index analysis for the highways in the state of Santa Catarina, Brazil, and produce a map considering that index and the areas susceptible to urban floods and landslides. The risk knowledge, combining hazard and vulnerability, is the first pillar of an Early Warning System, and represent an important tool for stakeholders from the transportation sector in a disaster risk reduction agenda.
Cities increasingly face flood risk primarily due to extensive changes of the natural land cover to built-up areas with impervious surfaces. In urban areas, flood impacts come mainly from road interruption. This paper proposes an urban flood risk map from hydrological and mobility data, considering the megacity of São Paulo, Brazil, as a case study. We estimate the flood susceptibility through the Height Above the Nearest Drainage algorithm; and the potential impact through the exposure and vulnerability components. We aggregate all variables into a regular grid and then classify the cells of …
Smart resilience is the beneficial result of the collision course of the fields of data science and urban resilience to flooding. The objective of this study is to propose and demonstrate a smart flood resilience framework that leverages heterogeneous community-scale big data and infrastructure sensor data to enhance predictive risk monitoring and situational awareness. The smart flood resilience framework focuses on four core capabilities that could be augmented by the use of heterogeneous community-scale big data and analytics techniques: (1) predictive flood risk mapping; (2) automated rapi…
Assessing The Spatially Heterogeneous Impact of Recurrent Flooding On Accessibility: A Case Study of The Hampton Roads Region:Part 2 Transit Accessibility
An agent-based model (ABM) for simulating flood-pedestrian interaction is augmented to particularly explore more realistic responses of evacuating pedestrians during flooding. Pedestrian agents within the ABM follow navigation rules of governing their movement in dry areas. When in floodwater, pedestrian agents are assigned extra behavioural rules to factor in their states of stability and walking speed, and their different body height and weight. The ABM is applied to replicate a synthetic test case of a flooded shopping centre, considering increasingly sophisticated configuration modes for t…
Urban flooding disrupts traffic networks, affecting the mobility and disrupting access of residents. Since flooding events are predicted to increase due to climate change, and given the criticality of traffic networks, understanding the flood-caused disruption of traffic networks is critical to improve emergency planning and city resilience. Leveraging high-resolution traffic network data from a major flood event and advanced high-order network analysis, this study reveals characteristics of the anatomy of perturbed traffic networks. First, the findings show network-wide persistent increased t…
The damage to residential buildings caused by hurricane-induced hazards is significant in the United States. There is a substantial body of research related to assessing physical damages to residential buildings and their direct economic impacts, yet the social consequences of such damages have generally been neglected. Since more socially vulnerable community members tend to live in more vulnerable buildings and neighborhoods, it is imperative to incorporate social vulnerability into community resilience assessment models. In this paper, a novel framework is proposed to create hurricane-induc…
The assessment of seismic risk in urban areas with high seismicity is certainly one of the most important problems that territorial managers have to face. A reliable evaluation of this risk is the basis for the design of both specific seismic improvement interventions and emergency management plans. Unappropriate seismic risk assessments may provide misleading results and induce bad decisions with relevant economic and social impact. The seismic risk in urban areas is mainly linked to three factors, namely, "hazard", "exposure" and "vulnerability". Hazard measures the potential of an earthquak…
Texas A&M University（德克萨斯A&M大学）；Texas A&M University at Galveston（德克萨斯A&M大学加尔维斯顿分校）；University of Delaware（特拉华大学）
The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models. Predictive flood monitoring of road network flooding status plays an essential role in community hazard mitigation, preparedness, and response activities. Existing studies related to the estimation of road inundations either lack observed road inundation data for model validations or focus mainly on road inundation exposure assessment based on flood maps. This study addresses this limitation by using crowdsourced and fine-grained tra…
Floods affected more than 2 billion people worldwide from 1998 to 2017 and their occurrence is expected to increase due to climate warming, population growth and rapid urbanization. Recent approaches for understanding the resilience of transportation networks when facing floods mostly use the framework of percolation but we show here on a realistic high-resolution flood simulation that it is inadequate. Indeed, the giant connected component is not relevant and instead, we propose to partition the road network in terms of accessibility of local towns and define new measures that characterize th…
Social vulnerability is the susceptibility of a community to be adversely impacted by natural hazards and public health emergencies, such as drought, earthquakes, flooding, virus outbreaks, and the like. Climate change is at the root of many recent natural hazards while the COVID-19 pandemic is still an active threat. Social vulnerability also refers to resilience, or the ability to recover from such adverse events. To gauge the many aspects of social vulnerability the US Center of Disease Control (CDC) has subdivided social vulnerabilities into distinct themes, such as socioeconomic status, h…
