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SCC-IRG Track 2: Scalable Modeling and Adaptive Real-time Trust-based Communication (SMARTc) System for Roadway Inundations in Flood-Prone Communities

SCC-IRG 第 2 轨：针对易受洪水影响的社区道路洪水的可扩展建模和自适应实时基于信任的通信 (SMARTc) 系统

基本信息

批准号：
1951745
负责人：
Khan Iftekharuddin
金额：
$ 148.34万
依托单位：
Old Dominion University Research Foundation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1951745&HistoricalAwards=false
关键词：
SCC IRG Track Scalable Modeling

项目摘要

The frequency of recurrent nuisance flooding (RNF) events is increasing and accelerating along much of the US East and Gulf Coasts due to Sea Level Rise (SLR) and high tides. These recurrent floods overwhelm the stormwater drainage systems, cause road closures, pose a major threat to the built infrastructure, and disrupt communities. In this research, the near real-time processing of RNF data, longer term prediction of RNF at both street and community scale and effective sharing of this information will instill trust in local drivers and improve safety in the community. The main objective of this Smart & Connected Communities (SCC) project is to develop a Scalable Modeling and Adaptive Real-time Trust-based communication (SMARTc) system for roadway inundation detection and monitoring. The SMARTc system will be evaluated for a flood-prone region in the City of Norfolk, Virginia, using data from the City’s cameras, tide gauges, and existing and new overland water level sensors in the field. By having access to such information in near real-time, citizens will be able to avoid driving through flooded roads, emergency vehicles can be rerouted around inundated roads, and cities will have a better understanding of flooding patterns and the needs to invest in storm-water and coastal flood protection systems. The team will also engage with RISE – a non-profit organization in Norfolk focused on helping businesses develop new solutions for coastal communities to adapt to SLR and RNF. This collaboration is expected to expedite scaling up methods and technologies, and future transition to practice. In addition, various educational and outreach opportunities are planned to increase the project impact. These include a regional forum with participants representing a broad range of stakeholders, design projects for ongoing NSF REU programs, integration of research outcomes into undergraduate and graduate classes, hands on activities for visiting high school students, interdisciplinary capstone projects, and presentation of a prototype system to minority middle school students. The results of this research will be shared to the local community in Norfolk to increase awareness of RNF and technologies for increasing resilience to RNF. Once deployed in the field in Norfolk, the solutions could provide hundred-thousands of citizens, businesses, and emergency services in Hampton Roads with accurate roadway conditions under RNF. These solutions could then be adopted by other communities across the nation, potentially helping millions. The expected outcomes of this project are directly relevant to Harnessing the Data Revolution component of the NSF’s Ten Big Ideas.This research will include the following tasks: (i) Novel machine learning algorithms for detecting floodwater extent and depth at street level in near real-time based on surveillance camera images collected under varying weather conditions; (ii) Hydrodynamic modeling integrating a coupled hydrologic-stormwater-coastal model to predict flood levels at street to community scales and real-time update of these predictions based on sensor and image data; (iii) Prediction of roadway capacities in real-time under partial inundations and correlation of floodwater depth and extent with driver behavior; and (iv) Effective communication of flood risk and road inundation to the public, leveraging granularity and uncertainty of flood information. The envisioned system will leverage sensor data and camera images for near real-time road inundation detection and will integrate the extracted dynamic information with hydrodynamic models for street to community-scale road inundation prediction. The outcome of the first task will yield near real-time learning model for street-scale RNF extent and depth recognition. The second task will yield an improved community-scale road network flood prediction model for RNF extent, depth, and flood duration using City camera and other sensor data. The third task will yield improved microscopic car-following models for partially flooded roadway segments so that the capacities and bottlenecks may be estimated and characterized accurately in near real-time. Finally, the fourth task is expected to offer effective ‘risk’ communication strategies for drivers using the RNF extent, depth. By having access to such information in near real-time, which is currently not available, citizens are expected to avoid driving through flooded roads, emergency vehicles can reroute around inundated roads, and cities will have a better understanding of flooding patterns and the needs to invest in storm-water and coastal flood protection systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由于海平面上升(SLR)和涨潮，美国东部和墨西哥湾沿岸的大部分地区反复发生的滋扰洪水(RNF)事件的频率正在增加和加速。这些反复发生的洪水淹没了雨水排水系统，导致道路封闭，对已建成的基础设施构成了重大威胁，并扰乱了社区。在这项研究中，对RNF数据的近实时处理，街道和社区规模的RNF的长期预测，以及这些信息的有效共享，将灌输对当地司机的信任，并改善社区的安全。这个智能互联社区(SCC)项目的主要目标是开发一个可扩展建模和基于信任的自适应实时通信(SMARTC)系统，用于道路淹没检测和监控。SMARTC系统将在弗吉尼亚州诺福克市的一个洪水多发地区进行评估，使用该市的摄像机、验潮仪以及现场现有的和新的陆上水位传感器的数据。通过近乎实时地获取这样的信息，市民将能够避免在被洪水淹没的道路上开车，紧急车辆可以在被洪水淹没的道路上改变路线，城市将更好地了解洪水模式，以及投资于暴雨和沿海防洪系统的需求。该团队还将与Rise合作，Rise是诺福克的一个非营利性组织，专注于帮助企业为沿海社区开发新的解决方案，以适应SLR和RNF。这种合作预计将加快扩大方法和技术的规模，并在未来过渡到实践。此外，还计划提供各种教育和外展机会，以增加项目的影响。其中包括一个代表广泛利益相关者的地区论坛，为正在进行的NSF REU项目设计项目，将研究成果整合到本科生和研究生课堂，为访问高中生而开展的实践活动，跨学科的顶峰项目，以及向少数族裔中学生展示原型系统。这项研究的结果将分享给诺福克的当地社区，以提高对RNF的认识和提高对RNF的复原力的技术。一旦部署在诺福克的现场，这些解决方案可以为数十万市民、企业和汉普顿道路上的紧急服务提供准确的道路条件。然后，这些解决方案可以被全国其他社区采用，可能会帮助数百万人。该项目的预期结果直接关系到NSF十大理想中的数据革命部分的利用。本研究将包括以下任务：(I)基于在不同天气条件下收集的监控摄像头图像，近实时地检测街道水平的洪水范围和深度的新型机器学习算法；(Ii)集成水文-暴雨-海岸耦合模型的水动力学建模，以预测街道到社区尺度的洪水水位，并基于传感器和图像数据实时更新这些预测；(Iii)部分淹没情况下道路通行能力的实时预测以及洪水深度和范围与驾驶员行为的关联；以及(Iv)利用洪水信息的粒度和不确定性，有效地向公众传达洪水风险和道路淹没情况。设想的系统将利用传感器数据和摄像机图像进行近实时的道路淹没检测，并将提取的动态信息与水动力学模型相结合，用于街道到社区规模的道路淹没预测。第一个任务的结果将产生街道尺度的RNF广度和深度识别的近实时学习模型。第二项任务将利用城市相机和其他传感器数据，建立一个改进的社区规模的道路网络洪水预测模型，用于预测RNF的范围、深度和洪水持续时间。第三项任务将为部分淹没的路段建立改进的微观跟车模型，以便可以近实时准确地估计容量和瓶颈。最后，第四项任务预计将使用RNF广度和深度为司机提供有效的风险沟通策略。通过近乎实时地获取此类信息(目前无法获得)，市民有望避免在被洪水淹没的道路上驾车行驶，应急车辆可以在被洪水淹没的道路周围改道，城市将更好地了解洪水模式以及投资于暴雨和沿海洪水防御系统的需求。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。