Understanding Network Dynamics in Flooding Emergencies for Urban Resilience

Many cities around the world are exposed to extreme flooding events. As a result of rapid population growth and urbanization, cities are also likely to become more vulnerable in the future and subsequently, more disruptions would occur in the face of flooding. Resilience, an ability of strong resistance to and quick recovery from emergencies, has been an emerging and important goal of cities. Uncovering mechanisms of flooding emergencies and developing effective tools to sense, communicate, predict and respond to emergencies is critical to enhancing the resilience of cities. To overcome this challenge, existing studies have attempted to conduct post-disaster surveys, adopt remote sensing technologies, and process news articles in the aftermath of disasters. Despite valuable insights obtained in previous literature, technologies for real-time and predictive situational awareness are still missing. This limitation is mainly due to two barriers. First, existing studies only use conventional data sources, which often suppress the temporal resolution of situational information. Second, models and theories that can capture the real-time situation is limited. To bridge these gaps, I employ human digital trace data from multiple data sources such as Twitter, Nextdoor, and INTRIX. My study focuses on developing models and theories to expand the capacity of cities in real-time and predictive situational awareness using digital trace data. In the first study, I developed a graph-based method to create networks of information, extract critical messages, and map the evolution of infrastructure disruptions in flooding events from Twitter. My second study proposed and tested an online network reticulation theory to understand how humans communicate and spread situational information on social media in response to service disruptions. The third study proposed and tested a network percolation-based contagion model to understand how floodwaters spread over urban road networks and the extent to which we can predict the flooding in the next few hours. In the last study, I developed an adaptable reinforcement learning model to leverage human trace data from normal situations and simulate traffic conditions during the flooding. All proposed methods and theories have significant implications and applications in improving the real-time and predictive situational awareness in flooding emergencies

Vulnerability analysis in complex networks under a flood risk reduction point of view

The measurement and mapping of transportation network vulnerability to natural hazards constitute subjects of global interest for a sustainable development agenda and as means of adaptation to climate change. During a flood, some elements of a transportation network can be affected, causing the loss of lives. Furthermore, impacts include damage to vehicles, streets/roads, and other logistics services - sometimes with severe economic consequences. 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 reducing the network’s average efficiency due to removing the set of edges related to that element. In this paper, we present the results of a systematic literature overview and a case study applying the topological vulnerability index for the highways in Santa Catarina (Brazil). We produce a map considering that index and areas susceptible to urban floods and landslides. Risk knowledge, combining hazard and vulnerability, is the first pillar of an Early Warning System and represents an important tool for stakeholders of the transportation sector in a disaster risk reduction agenda.Peer Reviewe

Quantification of Loss of Access to Critical Services during Floods in Greater Jakarta: Integrating Social, Geospatial, and Network Perspectives

This work presents a framework for assessing the socio-physical disruption of critical infrastructure accessibility using the example of Greater Jakarta, a metropolitan area of the Indonesian city. The first pillar of the framework is damage quantification based on the real flood event in 2020. Within this pillar, the system network statistics before and shortly after the flood were compared. The results showed that the flood impeded access to facilities, distorted transport connectivity, and increased system vulnerability. Poverty was found to be negatively associated with surface elevation, suggesting that urbanization of flood-prone areas has occurred. The second pillar was a flood simulation. Our simulations identified the locations and clusters that are more vulnerable to the loss of access during floods, and the entire framework can be applied to other cities and urban areas globally and adapted to account for different disasters that physically affect urban infrastructure. This work demonstrated the feasibility of damage quantification and vulnerability assessment relying solely on open and publicly available data and tools. The framework, which uses satellite data on the occurrence of floods made available by space agencies in a timely manner, will allow for rapid ex post investigation of the socio-physical consequences of disasters. It will save resources, as the analysis can be performed by a single person, as opposed to expensive and time-consuming ground surveys. Ex ante vulnerability assessment based on simulations will help communities, urban planners, and emergency personnel better prepare for future shocks

Towards interactive betweenness centrality estimation for transportation network using capsule network

Includes bibliographical references.2022 Fall.The node importance of a graph needs to be estimated for many graph-based applications. One of the most popular metrics for measuring node importance is betweenness centrality, which measures the amount of influence a node has over the flow of information in a graph. However, the computation complexity of calculating betweenness centrality is extremely high with large- scale graphs. This is especially true when analyzing the road networks of states with millions of nodes and edges, making it infeasible to calculate their betweenness centrality (BC) in real- time using traditional iterative methods. The application of a machine learning model to predict the importance of nodes provides opportunities to address this issue. Graph Neural Networks (GNNs), which have been gaining popularity in recent years, are particularly well-suited for graph analysis. In this study, we propose a deep learning architecture RoadCaps to estimate the BC by merging Capsule Neural Networks with Graph Convolutional Networks (GCN), a convolution operation based GNN. We target the effective aggregation of features from neighbor nodes to approximate the correct BC of a node. We leverage patterns capturing the strength of the capsule network to effectively estimate the node level BC from the high-level information generated by the GCN block. We further compare the model accuracy and effectiveness of RoadCaps with the other two GCN-based models. We also analyze the efficiency and effectiveness of RoadCaps for different aspects like scalability and robustness. We perform one empirical benchmark with the road network for the entire state of California. The overall analysis shows that our proposed network can provide more accurate road importance estimation, which is helpful for rapid response planning such as evacuation during wildfires and flooding

Effects of Population Co-location Reduction on Cross-county Transmission Risk of COVID-19 in the United States

The rapid spread of COVID-19 in the United States has imposed a major threat to public health, the real economy, and human well-being. With the absence of effective vaccines, the preventive actions of social distancing and travel reduction are recognized as essential non-pharmacologic approaches to control the spread of COVID-19. Prior studies demonstrated that human movement and mobility drove the spatiotemporal distribution of COVID-19 in China. Little is known, however, about the patterns and effects of co-location reduction on cross-county transmission risk of COVID-19. This study utilizes Facebook co-location data for all counties in the United States from March to early May 2020. The analysis examines the synchronicity and time lag between travel reduction and pandemic growth trajectory to evaluate the efficacy of social distancing in ceasing the population co-location probabilities, and subsequently the growth in weekly new cases. The results show that the mitigation effects of co-location reduction appear in the growth of weekly new cases with one week of delay. Furthermore, significant segregation is found among different county groups which are categorized based on numbers of cases. The results suggest that within-group co-location probabilities remain stable, and social distancing policies primarily resulted in reduced cross-group co-location probabilities (due to travel reduction from counties with large number of cases to counties with low numbers of cases). These findings could have important practical implications for local governments to inform their intervention measures for monitoring and reducing the spread of COVID-19, as well as for adoption in future pandemics. Public policy, economic forecasting, and epidemic modeling need to account for population co-location patterns in evaluating transmission risk of COVID-19 across counties.Comment: 12 pages, 7 figure

Network resilience

Many systems on our planet are known to shift abruptly and irreversibly from one state to another when they are forced across a "tipping point," such as mass extinctions in ecological networks, cascading failures in infrastructure systems, and social convention changes in human and animal networks. Such a regime shift demonstrates a system's resilience that characterizes the ability of a system to adjust its activity to retain its basic functionality in the face of internal disturbances or external environmental changes. In the past 50 years, attention was almost exclusively given to low dimensional systems and calibration of their resilience functions and indicators of early warning signals without considerations for the interactions between the components. Only in recent years, taking advantages of the network theory and lavish real data sets, network scientists have directed their interest to the real-world complex networked multidimensional systems and their resilience function and early warning indicators. This report is devoted to a comprehensive review of resilience function and regime shift of complex systems in different domains, such as ecology, biology, social systems and infrastructure. We cover the related research about empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also discuss some ambiguous definitions, such as robustness, resilience, and stability.Comment: Review chapter

Integrated Modeling of Long-Term Vegetation and Hydrologic Dynamics in Rocky Mountain Watersheds

Changes in forest structure resulting from natural disturbances, or managed treatments, can have negative and long lasting impacts on water resources. To facilitate integrated management of forest and water resources, a System for Long-Term Integrated Management Modeling (SLIMM) was developed. By combining two spatially explicit, continuous time models, vegetation patterns can be simulated forward in time based on management criteria. Output from the SIMPPLLE vegetation simulator are converted into landcover maps at every time-step and used to predict hydrologic watershed responses to time-series landcover change with the SWAT model. Long-term watershed responses to vegetation management scenarios can therefore be evaluated from both terrestrial and hydrologic perspectives. Watersheds are common landscape analysis units, but vegetation dynamics within them do not function in isolation. Repeated century spanning SIMPPLLE simulations produced succession patterns that were significantly different in 84% of analysis watersheds when each was considered in isolation and within their landscape context. Watersheds with \u3e30% internal forest cover, and \u3c10% barren ground along their perimeters were more connected to landscape processes than those with more barren boundaries, and less forest cover within them. Calibration of SWAT was based on four years of streamflow and climate data recorded within the Tenderfoot Creek Experimental Forest research watershed. Validation with an additional four years used both traditional and objective regression-based hypothesis testing procedures. Adjustment of snow process, surface runoff lag, and groundwater recession parameters contributed most significantly to model calibration. Results confirm that when calibrated in a forested mountain watershed having snow-dominated hydrology, SWAT can predict annual, monthly and daily streamflow with high levels of accuracy and efficiency. For demonstration, SLIMM was used to evaluate natural and fire-suppressed forest management alternatives over a 300-year period. Compared to natural development, fire suppression created larger stand sizes, greater levels of aggregation, and increased the likelihood of process propagation across the landscape. Averaged over all simulations, fire suppression reduced annual water yield by up to 3%, streamflow variability by a factor of four, and the magnitude of annual peak flows by 15%. Literature supported results highlight the applicability of SLIMM as a management tool