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Skip Navigation LinksPBSJ > Our Businesses > Consulting > Specialty Markets > Homeland Security > HAZUS Multihazard Loss Estimation Project
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HAZUS Multihazard Loss Estimation Project 
Location: Washington, D.C. 
Client:     Federal Emergency Management Agency (FEMA) 

HAZUS-MH is the Department of Homeland Security's (DHS) national multihazard risk assessment model for quantifying the potential impacts from earthquake, flood, and hurricane disasters and has recently been used to analyze manmade disasters. Using a third-party module interface, HAZUS-MH is capable of interfacing with numerous other manmade disaster models to assess explosive blasts, WMD plumes, agriterrorism infectious diseases, and urban conflagrations. It is being developed through the National Institute of Building Sciences (NIBS) with funding from DHS.

As the successful developer of the original HAZUS models, data and software application, PBS&J was awarded the contract to develop the new HAZUS-MH application and supporting data modeling utilities. The firm was also charged with updating the national GIS-based hazard and inventory databases that provide the foundation for the model's accuracy and reliability. To support the scientific credibility of HAZUS-MH models, NIBS maintains committees of wind, flood, earthquake, and software experts to provide technical oversight and guidance to PBS&J.

The development of HAZUS-MH involves state-of-the-art GIS software (ArcGIS ArcView 8.3) to map and display hazard data and the results of damage and economic loss estimates for building and infrastructure. HAZUS-MH provides an extensive amount of GIS data, including building stock, critical facilities, transportation systems, utility systems, and population demographics and migration characteristics. The experience gained through the development of the national GIS data associated with HAZUS allows PBS&J to assist states and local communities with the actual data development process, the evaluation of the quality of the databases, and the identification of any deficiencies that exist in the data.

 
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