An Agent-Based Approach to Integrate Human Dynamics Into Disaster Risk Management

Abstract

Disaster risk management (DRM) is the application of disaster risk reduction policies and strategies to reduce existing disaster risk and manage residual risk. However, due to dynamic human factors, it is challenging to depict and assess the effectiveness of DRM measures, and their implementation usually lacks a sufficient evidence-based evaluation process. Therefore, this study developed an agent-based model to integrate dynamic human behaviors into the DRM measures and evaluated their effectiveness in casualty reduction. The model was calibrated to simulate the debris flow event at Longchi town, China in 13 August 2010. The early warning system (EWS) and related DRM measures were taken as examples. The effectiveness of different DRM measures was quantitated by comparing the number of potential casualties. The main findings were: 1) EWS was very effective for community-based DRM as it could significantly decrease the average casualties by 30%. 2) Credibility of EWS was critical to its effectiveness. Less credible EWS might reduce its effectiveness by 9%. 3) EWS could be supplemented by other measures to further reduce casualties by 6%. 4) The downside effects of other DRM measures to EWS might exist and reduce its effectiveness by up to 5%. This study put forward an evidence-based approach to help policymakers select more cost-effective DRM measure, especially in the less developed countries where the available resources for DRM are limited.