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An Integrated Social Science and Agent-based Modeling Approach to Improve Life Safety from Near-field Tsunami Hazards

An Integrated Social Science and Agent-based Modeling Approach to Improve Life Safety from Near-field Tsunami Hazards
综合社会科学和基于主体的建模方法,以提高近场海啸灾害的生命安全
批准号:
1563618
负责人:
Haizhong Wang
金额:
$39.34万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-06-01 至 2020-05-31

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中文摘要
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英文摘要
This project will develop fundamental understanding of how integrated social science and agent-based modeling approaches can improve life safety under threat of near-field tsunami hazards. The targeted hazard scenario is a magnitude 9.0 earthquake and tsunami from the Cascadia Subduction Zone, threatening communities along 1,000 miles of the US Pacific Northwest coastline. This project develops close collaboration with a number of organizations responsible for public safety during the response extreme natural hazards, including the Oregon Department of Transportation, Oregon Office of Emergency Management, Oregon Parks and Recreation, and Oregon Sea Grant. Social science data from diverse groups will contribute to a growing body of knowledge on evacuation decision-making. A collaboration with the Oregon Museum of Science of Industry (OMSI) will leverage the museum's ongoing activities, including those which reach underrepresented groups. Modeling source code will be shared and distributed.The project integrates the disciplines of sociology with engineering to investigate multiple forms of tsunami information and its potential impact on evacuation decision-making by the general public and the unique professional community responsible for coastal visitor safety. The project objectives are (1) to identify the cognitive and social factors that influence multimodal evacuation decision-making behavior; (2) to utilize an Agent-Based Modeling framework to provide realistic/credible hypothetical scenarios for near-field tsunamis hazards; and (3) to determine the usability/acceptability of social science-informed agent-based modeling for understanding evacuation decision-making. This work will therefore improve understand of how decision-making affects life safety, with broad outreach to diverse audiences both to assess the feasibility of this approach and for purposes of outreach.
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