Design parameter exploration for community-based flood early warning system with dynamic probabilistic performance assessment approach

Developing regions are vulnerable to disaster with limited ICT technology, and there are challenges in reducing flood damage through early warning systems. As a countermeasure, Community-based Flood Early Warning System (CBFEWS) based on sociotechnical systems developed by integrating low-cost technologies and human-centered communication networks that do not rely solely on governmental early warnings is being promoted by several disaster relief organizations as an affordable option in developing regions. While the effectiveness of CBFEWS has been proven, challenges remain in maintaining the effectiveness of the system over the long term. This study proposes a model-driven design parameter exploration method for CBFEWS-implementing organizations to develop strategies for sustaining the effectiveness of CBFEWS over years. The dynamic probabilistic performance evaluation is designed based on probabilistic risk assessment (PRA) and the proposal assists in identifying factors that are sensitive to successfully maintaining system effectiveness. The factors are selected based on a sociotechnical systems perspective such as social preparedness, component failure rate, and system performance. Based on the output from this model, organizations can design, operate, and maintain effective CBFEWS and strengthen system resilience. This paper demonstrates the proposed methodology to show how the model-driven design parameter exploration can facilitate a discussion of increasing CBFEWS sustainability and resiliency.