Dissertation/Thesis Abstract

Bridging scientific model outputs with emergency response needs in catastrophic earthquake responses
by Johannes, Tay W., Ph.D., The George Washington University, 2010, 161; 3397309
Abstract (Summary)

In emergency management, scientific models are widely used for running hazard simulations and estimating losses often in support of planning and mitigation efforts. This work expands utility of the scientific model into the response phase of emergency management. The focus is on the common operating picture as it gives context to emergency response information needs. Taking a focused approach on the emergency support function of mass care, emergency assistance, housing, and human services, the key element in the common operating picture is identified as the damage or disaster assessment. The research originates with conference and workshop results coming from Geneva, Switzerland, involving the International Federation of the Red Cross/Red Crescent societies. In general, the conference identified decisions, actions and critical success factors that were based on various disaster scenarios. It also highlighted the sensitivity of the response system to issues of context, timing, and function. These conference findings establish the baseline of this research. Continuing the response focus, this research applies expert opinion to develop a methodology for using rational consensus to develop an information relevance model in emergency response.

One commonly used scientific model in United States emergency management is a loss estimation program known as HAZUS-MH. Developed under Federal Emergency Management Agency direction, HAZUS-MH offers information capabilities useful to emergency management professionals in consideration for estimating damage, loss, and economic impact due to floods, earthquakes, and hurricanes. In any type of disaster, emergency managers need a comprehensive perspective—a common operating picture—to develop a situational awareness critical to decision making. Often in major disasters, communication systems are damaged and early response efforts are frustrated by broken and degraded networks. The HAZUS-MH scientific model meets important application criteria of operational familiarity and wide-spread usage to address common operating picture approaches prior to communication recovery. The significance of HAZUS-MH meeting these criteria rests in the opportunity to have uniformity in decision-supporting information at geographically separated locations.

The methodology required scenario development and simulation to establish context for the expert elicitation process. Because HAZUS-MH offers fifty-four categories of information factors, the information hierarchy process required an initial screening as a first stage. Using the catastrophic earthquake response context, experts screened the HAZUS-MH information factors to determine the most relevant mass care response information needs. This resulted in eleven information factors; the factors were then presented to a new group of experts in a scenario-based stakeholder preference study that established preference based on the single criteria of relevance. The specific expert elicitation method used psychological scaling to establish scores for each HAZUS-MH information factor. Using the Thurstone C model to establish upper and lower confidence intervals for each score, the study offers information hierarchy findings and conclusions for the mass care function‘s initial response.

Indexing (document details)
Advisor: Jefferson, Theresa L.
Commitee: Deason, Jonathan P., Harrald, John R., Mazzuchi, Thomas A., Smith, David A.
School: The George Washington University
Department: Engineering Mgt and Systems Engineering
School Location: United States -- District of Columbia
Source: DAI-A 71/04, Dissertation Abstracts International
Subjects: Information science, Systems science, Operations research
Keywords: Disaster response, Earthquakes, Emergency response, HAZUS, Information, Mass care, Scientific models
Publication Number: 3397309
ISBN: 978-1-109-69114-6
Copyright © 2020 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy