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SUMMARY:Developing a Framework to Optimize Floodnet Sensor Deployments Around NYC for Equitable and Impact-Based Hyper-Local Street-Level Flood Monitoring and Data Collection
DESCRIPTION:Urban floods can lead to significant damages\, and accurately measuring flood extents and depths is vital for effective management. Traditional flood measurement methods can be costly and may not provide detailed mapping. Recent advancements in affordable flood-monitoring sensors offer real-time measurements\, but there’s a challenge in prioritizing where to place them\, especially when considering equity and the needs of various stakeholders. This research introduced a framework that uses expert input to determine potential uses for these sensors and metrics to decide where to deploy them. The method was tested in New York City and was found to be more effective than basic prioritization methods\, ensuring stakeholder needs and equity concerns were met. This framework can help local governments prioritize sensor locations for better flood management\, promoting resilient and equitable cities. Future studies can adapt and test this approach in different urban settings. \nPresented by NYU’s Riccardo Negri and Prof. Luis Ceferino
URL:https://c2smart.engineering.nyu.edu/event/developing-a-framework-to-optimize-floodnet-sensor-deployments-around-nyc-for-equitable-and-impact-based-hyper-local-street-level-flood-monitoring-and-data-collection/
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