TY - GEN
T1 - Design parameter exploration for community-based flood early warning system with dynamic probabilistic performance assessment approach
AU - Tomita, Yuki
AU - Kohtake, Naohiko
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - community-based flood early warning system (CBFEWS)
KW - dynamic probabilistic performance assessment
KW - engineered system
KW - sensitivity analysis
KW - sociotechnical systems
UR - http://www.scopus.com/inward/record.url?scp=85161844623&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85161844623&partnerID=8YFLogxK
U2 - 10.1109/SysCon53073.2023.10131274
DO - 10.1109/SysCon53073.2023.10131274
M3 - Conference contribution
AN - SCOPUS:85161844623
T3 - SysCon 2023 - 17th Annual IEEE International Systems Conference, Proceedings
BT - SysCon 2023 - 17th Annual IEEE International Systems Conference, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th Annual IEEE International Systems Conference, SysCon 2023
Y2 - 17 April 2023 through 20 April 2023
ER -