Unraveling the complexity of human behavior and urbanization on community vulnerability to floods.

Floods are among the costliest natural hazards and their consequences are expected to increase further in the future due to urbanization in flood-prone areas. It is essential that policymakers understand the factors governing the dynamics of urbanization to adopt proper disaster risk reduction techniques. Peoples' relocation preferences and their perception of flood risk (collectively called human behavior) are among the most important factors that influence urbanization in flood-prone areas. Current studies focusing on flood risk assessment do not consider the effect of human behavior on urbanization and how it may change the nature of the risk. Moreover, flood mitigation policies are implemented without considering the role of human behavior and how the community will cope with measures such as buyout, land acquisition, and relocation that are often adopted to minimize development in flood-prone regions. Therefore, such policies may either be resisted by the community or result in severe socioeconomic consequences. In this study, we present a new Agent-Based Model (ABM) to investigate the complex interaction between human behavior and urbanization and its role in creating future communities vulnerable to flood events. We identify critical factors in the decisions of households to locate or relocate and adopt policies compatible with human behavior. The results show that when people are informed about the flood risk and proper incentives are provided, the demand for housing within 500-year floodplain may be reduced as much as 15% by 2040 for the case study considered. On the contrary, if people are not informed of the risk, 29% of the housing choices will reside in floodplains. The analyses also demonstrate that neighborhood quality-influenced by accessibility to highways, education facilities, the city center, water bodies, and green spaces, respectively-is the most influential factor in peoples' decisions on where to locate. These results provide new insights that may be used to assist city planners and stakeholders in examining tradeoffs between costs and benefits of future land development in achieving sustainable and resilient cities.

Impact of multiple waves of COVID-19 on healthcare networks in the United States.

The risk of overwhelming hospitals from multiple waves of COVID-19 is yet to be quantified. Here, we investigate the impact of different scenarios of releasing strong measures implemented around the U.S. on COVID-19 hospitalized cases and the risk of overwhelming the hospitals while considering resources at the county level. We show that multiple waves might cause an unprecedented impact on the hospitals if an increasing number of the population becomes susceptible and/or if the various protective measures are discontinued. Furthermore, we explore the ability of different mitigation strategies in providing considerable relief to hospitals. The results can help planners, policymakers, and state officials decide on additional resources required and when to return to normalcy.

Orchestrating performance of healthcare networks subjected to the compound events of natural disasters and pandemic.

The current COVID-19 pandemic has demonstrated the vulnerability of healthcare systems worldwide. When combined with natural disasters, pandemics can further strain an already exhausted healthcare system. To date, frameworks for quantifying the collective effect of the two events on hospitals are nonexistent. Moreover, analytical methods for capturing the dynamic spatiotemporal variability in capacity and demand of the healthcare system posed by different stressors are lacking. Here, we investigate the combined impact of wildfire and pandemic on a network of hospitals. We combine wildfire data with varying courses of the spread of COVID-19 to evaluate the effectiveness of different strategies for managing patient demand. We show that losing access to medical care is a function of the relative occurrence time between the two events and is substantial in some cases. By applying viable mitigation strategies and optimizing resource allocation, patient outcomes could be substantially improved under the combined hazards.

Resilience of School Systems Following Severe Earthquakes.

Natural disasters may have catastrophic and long-lasting impacts on communities' physical, economic, and social infrastructure. Slow recovery of educational services following such events is likely to cause traumatic stress in children, lead families to out-migrate, and affect the community's overall social stability. Methods for quantifying and assessing the restoration process of educational systems and their dependencies on other supporting infrastructure have not received adequate attention. This study introduces, for the first time, a new framework to evaluate the functionality, recovery, and resilience of a school system following severe earthquake events. The framework considers both the quantity and quality of education services provided, school enrollment, and staff employment, as well as the interaction between various agents such as staff, students, parents, administration, and community. A virtual testbed community, Centerville, is utilized to highlight the application of this framework. The impact of school reopening policies on the number of students enrolled as well as the potential for homeschooling is also considered. The availability of various enrollment alternatives for students, backup classroom space and functioning utility systems, and facilitation of staff and supplies transfer between schools substantially increase the resilience of the education service.

The Role of Urban Growth in Resilience of Communities Under Flood Risk.

Flood risk to urban communities is increasing significantly as a result of the integrated effects of climate change and socioeconomic development. The latter effect is one of the main drivers of rising flood risk has received less attention in comparison to climate change. Economic development and population growth are major causes of urban expansion in flood-prone areas, and a comprehensive understanding of the impact of urban growth on flood risk is an essential ingredient of effective flood risk management. At the same time, planning for community resilience has become a national and worldwide imperative in recent years. Enhancements to community resilience require well-integrated and enormous long-term public and private investments. Accordingly, comprehensive urban growth plans should take rising flood risk into account to ensure future resilient communities through careful collaboration between engineers, geologists, socialists, economists, and urban planners within the framework of life-cycle analysis. This paper highlights the importance of including urban growth in accurate future flood risk assessment and how planning for future urbanization should include measurement science-based strategies in developing policies to achieve more resilient communities.

Interpreting the socio-technical interactions within a wind damage-artificial neural network model for community resilience.

The use of machine learning has grown in popularity in various disciplines. Despite the popularity, the apparent 'black box' nature of such tools continues to be an area of concern. In this article, we attempt to unravel the complexity of this black box by exploring the use of artificial neural networks (ANNs), coupled with graph theory, to model and interpret the spatial distribution of building damage from extreme wind events at a community level. Structural wind damage is a topic that is mostly well understood for how wind pressure translates to extreme loading on a structure, how debris can affect that loading and how specific social characteristics contribute to the overall population vulnerability. While these themes are widely accepted, they have proven difficult to model in a cohesive manner, which has led primarily to physical damage models considering wind loading only as it relates to structural capacity. We take advantage of this modelling difficulty to reflect on two different ANN models for predicting the spatial distribution of structural damage due to wind loading. Through graph theory analysis, we study the internal patterns of the apparent black box of artificial intelligence of the models and show that social parameters are key to predict structural damage.