StormSense: A New Integrated Network of IoT Water Level Sensors in the Smart Cities of Hampton Roads, VA
AbstractPropagation of cost-effective water level sensors powered through the Internet of Things (IoT) has expanded the available offerings of ingestible data streams at the disposal of modern smart cities. StormSense is an IoT-enabled inundation forecasting research initiative and an active partici...
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Veröffentlicht in: | Marine Technology Society journal 2018-03, Vol.52 (2), p.56-67 |
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Zusammenfassung: | AbstractPropagation of cost-effective water level sensors powered through the Internet of Things (IoT) has expanded the available offerings of ingestible data streams at the disposal of modern smart cities. StormSense is an IoT-enabled inundation forecasting research initiative
and an active participant in the Global City Teams Challenge, seeking to enhance flood preparedness in the smart cities of Hampton Roads, VA, for flooding resulting from storm surge, rain, and tides. In this study, we present the results of the new StormSense water level sensors to help establish
the "regional resilience monitoring network" noted as a key recommendation from the Intergovernmental Pilot Project. To accomplish this, the Commonwealth Center for Recurrent Flooding Resiliency's Tidewatch tidal forecast system is being used as a starting point to integrate the
extant (NOAA) and new (United States Geological Survey [USGS] and StormSense) water level sensors throughout the region and demonstrate replicability of the solution across the cities of Newport News, Norfolk, and Virginia Beach within Hampton Roads, VA. StormSense's network employs a mix
of ultrasonic and radar remote sensing technologies to record water levels during 2017 Hurricanes Jose and Maria. These data were used to validate the inundation predictions of a street level hydrodynamic model (5-m resolution), whereas the water levels from the sensors and the model were
concomitantly validated by a temporary water level sensor deployed by the USGS in the Hague and crowd-sourced GPS maximum flooding extent observations from the sea level rise app, developed in Norfolk, VA. |
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ISSN: | 0025-3324 1948-1209 |
DOI: | 10.4031/MTSJ.52.2.7 |