Temporary housing plays a critical role in disaster response and recovery by providing a temporary home for displaced people before they return to their permanent residence. In recent years, temporary housing has faced three primary dilemmas related to design type, site selection, and cost. Significant contributions have been made in research and in practice to improve temporary housing management. However, gaps still exist in resolving the dilemmas, and a critical review and evaluation of current methods is needed to determine the path forward and identify priorities of future research. This paper presents a comprehensive overview of prior methods developed and applied towards temporary housing management and identifies future pathways for success in temporary housing research and implementation. The literature review reveals that temporary housing requires further research in proactive management, storage, sustainability, and community resilience to effectively enhance post-disaster temporary housing. This study finds that programs such as the Leadership in Energy and Environmental Design (LEED) and the Sheltering and Temporary Essential Power (STEP) program provide methodologies which can benefit temporary housing implementation, designs, and modeling. In addition, circular economy thinking can enable the recyclability of temporary housing to reduce economic and environmental impacts.

The concept of sustainable housing can take many forms or definitions. The view embraced in this study for sustainable housing was a structure that is low-cost, energy efficient, and one that uses local renewable or recycled materials, while at the same time being safe and providing shelter to its occupants from the elements. Rather than focus on a few aspects of housing in isolation from spatial necessities or occupant demographics, the approach used in this study was to seek best practices for sustainable temporary housing that also accounts for local climatic conditions. The global need for temporary housing may be expected to increase due to multiple factors: increasing severity of natural disasters resulting in large number of displaced peoples; an increase in the number of climate refugees as a result of a changing climate; and the inability of developing countries to provide sufficient infrastructure in pace with their rapid population growth. Novel developments and promising products and methods in the field of temporary disaster relief housing have been reviewed in the study. To date, an engineered solution to the problem that is low-cost, quick to construct, environmentally and socially sustainable, takes into account the needs of the occupant, and accounts for local climatic conditions has not been found. On the contrary, every major disaster proved the inability of governments to cope with the challenges posed with providing for the large numbers of displaced peoples

A spatially-resolved understanding of the intensity of a flood hazard is required for accurate predictions of infrastructure reliability and losses in the aftermath. Currently, researchers who wish to predict flood losses or infrastructure reliability following a flood usually rely on computationally intensive hydrodynamic modeling or on flood hazard maps (e.g., the 100-year floodplain) to build a spatially-resolved understanding of the flood’s intensity. However, both have specific limitations. The former requires both subject matter expertise to create the models and significant computation time, while the latter is a static metric that provides no variation among specific events. The objective of this work is to develop an integrated data-driven approach to rapidly predict flood damages using two emerging flood intensity heuristics, namely the Flood Peak Ratio (FPR) and NASA’s Giovanni Flooded Fraction (GFF). This study uses data on flood claims from the National Flood Insurance Program (NFIP) to proxy flood damage, along with other well-established flood exposure variables, such as regional slope and population. The approach uses statistical learning methods to generate predictive models at two spatial levels: nationwide and statewide for the entire contiguous United States. A variable importance analysis demonstrates the significance of FPR and GFF data in predicting flood damage. In addition, the model performance at the state-level was higher than the nationwide level analysis, indicating the effectiveness of both FPR and GFF models at the regional level. A data-driven approach to predict flood damage using the FPR and GFF data offer promise considering their relative simplicity, their reliance on publicly accessible data, and their comparatively fast computational speed.