Emergency managers are faced with the challenge of acting quickly after a hurricane but rarely have detailed information available about type and amount of damage. In response to this need, linear additive geospatial models based on logistic regression analyses of driving variables including wind, rain, surge, topography were developed and automation routines programmed that rapidly and accurately predict a variety of damage types. Since a preponderance of damage is associated with falling trees, over 2000 post-Katrina forested plots were used to fit and validate independent models for hardwood blowdown and pine shear. Additional models using peak wind gusts and maximum sustained winds respectively were fully automated. Most importantly, total model run time was decreased from 36 to 5 hours for the more complicated forest damage models. The models have been vetted by the Mississippi Emergency Management Agency (MEMA) and will be part of MEMA’s hurricane action response plans.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-1148 |
| Date | 06 August 2011 |
| Creators | Vaughan, Ryan Christopher |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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