An Agent‐Based Model of Evolving Community Flood Risk. Issue 6 (17th November 2017)

• Record Type: Journal Article
• Authors: Tonn, Gina L.; Guikema, Seth D.
• Is Part Of: Risk analysis. Volume 38:Issue 6(2018)
• Page Start: 1258
• Page End: 1278
• Publication Date: 2017-11-17
• Subjects: Agent‐based model -- flood risk -- flood mitigation; Technology -- Risk assessment -- Periodicals; 658.403
• DOI: 10.1111/risa.12939
• Languages: English
• ISSNs: 0272-4332

Abstract

Abstract: Although individual behavior plays a major role in community flood risk, traditional flood risk models generally do not capture information on how community policies and individual decisions impact the evolution of flood risk over time. The purpose of this study is to improve the understanding of the temporal aspects of flood risk through a combined analysis of the behavioral, engineering, and physical hazard aspects of flood risk. Additionally, the study aims to develop a new modeling approach for integrating behavior, policy, flood hazards, and engineering interventions. An agent‐based model (ABM) is used to analyze the influence of flood protection measures, individual behavior, and the occurrence of floods and near‐miss flood events on community flood risk. The ABM focuses on the following decisions and behaviors: dissemination of flood management information, installation of community flood protection, elevation of household mechanical equipment, and elevation of homes. The approach is place based, with a case study area in Fargo, North Dakota, but is focused on generalizable insights. Generally, community mitigation results in reduced future damage, and individual action, including mitigation and movement into and out of high‐risk areas, can have a significant influence on community flood risk. The results of this study provide useful insights into the interplay between individual and community actions and how it affects the evolution of flood risk. This study lends insight into priorities for future work, including the development of more in‐depth behavioral and decision rules at the individual and community level.