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A Network Observability Framework for Sensor Placement in Flood Control Networks to Improve Flood Situational Awareness and Risk Management

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  • Farahmand, Hamed
  • Liu, Xueming
  • Dong, Shangjia
  • Mostafavi, Ali
  • Gao, Jianxi

Abstract

Monitoring the state of infrastructure systems proactively is crucial to ensure their proper functionality during extreme events. Flood control networks are designed to keep communities safe from inundation. Accurately monitoring the inundation status of flood control components could enhance flood situational awareness and risk management during extreme events. However, the design and placement of sensor networks that collect data to monitor the status of flooding do not often consider the principles of observability. We bridge the gap by creating a framework for the assessment of flood control network observability and determining the minimum number and locations of the flood gauges to achieve maximum monitoring across critical regions. We first delineate critical regions that are needed to be observed, then identify feasible solutions on sensor sets, and finally determine the candidate sensor set based on the importance of the nodes that exist in each sensor set. We tested the framework in the context of Harris County, Texas, as a case study. The results show that the current flood gauge placement is not sufficient for comprehensively monitoring the flooding status of critical areas, nor informed by the network observability principles. Results also offer insights to decision-makers to extend the current flood gauge network or design new flood gauge networks more effectively.

Suggested Citation

  • Farahmand, Hamed & Liu, Xueming & Dong, Shangjia & Mostafavi, Ali & Gao, Jianxi, 2022. "A Network Observability Framework for Sensor Placement in Flood Control Networks to Improve Flood Situational Awareness and Risk Management," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
  • Handle: RePEc:eee:reensy:v:221:y:2022:i:c:s0951832022000448
    DOI: 10.1016/j.ress.2022.108366
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    3. Yin, Xiuxian & He, Wei & Cao, You & Ma, Ning & Zhou, Guohui & Li, Hongyu, 2024. "A new health state assessment method based on interpretable belief rule base with bimetric balance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    4. Gangwal, Utkarsh & Dong, Shangjia, 2022. "Critical facility accessibility rapid failure early-warning detection and redundancy mapping in urban flooding," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    5. Wang, Mengmeng & Incecik, Atilla & Feng, Shizhe & Gupta, M.K. & Królczyk, Grzegorz & Li, Z, 2023. "Damage identification of offshore jacket platforms in a digital twin framework considering optimal sensor placement," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

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