An Intelligent Cyberinfrastructure for IoT Sensing, Flood and Storm water Monitoring and Modeling Systems (Intelligent Riverâ Initiative)
Abstract
Leveraging recent advances in technologies surrounding the Internet of Things, "smart" flood and stormwater systems are poised to transform watershed management by enabling ubiquitous real-time sensing and control. Recent applications have demonstrated the potential to improve flood forecasting, stormwater management, and prevent road flooding and sewer overflow. However, adoption of smart flood and stormwater systems has been hindered by a limited number of successful case studies, along with a lack of guidance on how smart flood and stormwater systems should be designed and built. This research introduces Intelligent Riverâ Initiative— an open-source, end-to-end platform for real-time monitoring, modeling and control of a rapid response watershed system in South Carolina. Intelligent Riverâ Initiative includes (i) a field-deployed sensor fabric and uplink infrastructure, (ii) real-time data streaming middleware, and (iii) repository, presentation, and a cloud services platform. We illustrate the capabilities of the Intelligent Riverâ platform through two ongoing developments: (i) a high-resolution flood monitoring and modeling network that detects and communicates flood hazards at the level of individual roadways, and (ii) a real-time stormwater control network that actively monitors and modulates water quality to reduce stream erosion. Through these case studies, we demonstrate the real-time potential for smart flood and stormwater systems to enable sustainable management and control of water resources systems across a mixed urban-rural watershed.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMH177...02S
- Keywords:
-
- 1816 Estimation and forecasting;
- HYDROLOGY;
- 1821 Floods;
- HYDROLOGY;
- 1964 Real-time and responsive information delivery;
- INFORMATICS;
- 4333 Disaster risk analysis and assessment;
- NATURAL HAZARDS