Agent-based Modeling to Evaluate Human–Environment Interactions in Community Flood Risk Mitigation

Yu Han*, Liang Mao, Xuqi Chen, Wei Zhai, Zhong-Ren Peng, Pallab Mozumder

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This article deals with household-level flood risk mitigation. We present an agent-based modeling framework to simulate the mechanism of natural hazard and human interactions, to allow evaluation of community flood risk, and to predict various adaptation outcomes. The framework considers each household as an autonomous, yet socially connected, agent. A Beta–Bernoulli Bayesian learning model is first applied to measure changes of agents’ risk perceptions in response to stochastic storm surges. Then the risk appraisal behaviors of agents, as a function of willingness-to-pay for flood insurance, are measured. Using Miami-Dade County, Florida as a case study, we simulated four scenarios to evaluate the outcomes of alternative adaptation strategies. Results show that community damage decreases significantly after a few years when agents become cognizant of flood risks. Compared to insurance policies with pre-Flood Insurance Rate Maps subsidies, risk-based insurance policies are more effective in promoting community resilience, but it will decrease motivations to purchase flood insurance, especially for households outside of high-risk areas. We evaluated vital model parameters using a local sensitivity analysis. Simulation results demonstrate the importance of an integrated adaptation strategy in community flood risk management.
Original languageEnglish
Number of pages21
JournalRisk Analysis
DOIs
Publication statusE-pub ahead of print - 12 Nov 2021

Scopus Subject Areas

  • Safety, Risk, Reliability and Quality
  • Physiology (medical)

User-Defined Keywords

  • Adaptation
  • agent-based model
  • flood risk mitigation
  • protection motivation theory

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