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StormSense: A New Integrated Network of IoT Water Level Sensors in the Smart Cities of Hampton Roads VA

机译:StormSense:弗吉尼亚州汉普顿路智能城市中的新型物联网水位传感器集成网络

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摘要

Propagation 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 (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), while 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.
机译:通过物联网(IoT)驱动的具有成本效益的水位传感器的普及,已经扩展了可用于现代智能城市的可消化数据流的可用产品。 StormSense是一项基于IoT的洪水预报研究计划,并且是“全球城市团队挑战赛”的积极参与者,该挑战赛旨在增强弗吉尼亚州汉普顿路智能城市的防洪能力,以应对因风暴潮,雨水和潮汐引起的洪水。在这项研究中,我们介绍了新型StormSense水位传感器的结果,以帮助建立“区域弹性监测网络”,这是政府间试点项目的一项重要建议。为了实现这一目标,英联邦经常性洪水抵御能力中心的Tidewatch潮汐预报系统被用作整合整个区域内现有(NOAA)和新型(USGS和StormSense)水位传感器的起点,并展示了该解决方案在整个地区的可重复性弗吉尼亚州汉普顿路内的纽波特纽斯,诺福克和弗吉尼亚海滩。 StormSense的网络结合了超声波和雷达遥感技术,以记录2017年飓风何塞和玛丽亚期间的水位。这些数据用于验证街道级水动力模型(5米分辨率)的淹没预测,而传感器和模型的水位则由USGS在海牙部署的临时水位传感器进行了验证,以及从弗吉尼亚州诺福克开发的Sea Level Rise应用程序中众包的GPS最大洪泛观测。

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