| description abstract | This study develops a methodological framework to prioritize urban areas for the deployment of hyper-local, real-time flood-monitoring sensors. These sensors promise enhanced flood risk management in cities, where flood hazards can be highly localized. Given that the sensors provide data limited to specific locations, cities need to prioritize their deployment in areas that maximize the potential uses of the data, in line with the varied objectives of multiple urban stakeholders. To address this need, the proposed framework integrates stakeholder elicitation on sensor usage and a range of associated metrics for guiding decision making on sensor deployment; combines the metrics with publicly available data through probabilistic risk analysis; and identifies high-priority areas for sensor deployment. The framework is tested in a case study in New York City (NYC), incorporating input from 45 stakeholders across different sectors, such as government agencies and research institutions. The case study identifies 32 potential uses of hyper-local sensors and 58 corresponding sensor deployment prioritization metrics that address flood risk management, community welfare, and critical infrastructure protection. These metrics are then characterized to identify areas in NYC where the deployment of sensors would be most beneficial/effective. In summary, the proposed framework bridges the gap between rapid advancements in the development of hyper-local sensors and strategies for their deployment, integrating stakeholder feedback and probabilistic risk analysis to provide cities with a unique decision-making tool for sensor deployment that is adaptable to diverse urban contexts. | |
