| Title |
Flood Monitoring And Mitigation Strategies For Flood-Prone Urban Areas |
| ID_Doc |
26682 |
| Authors |
Finley P.; Gatti G.; Goodall J.; Nelson M.; Nicholson K.; Shah K. |
| Year |
2020 |
| Published |
2020 Systems and Information Engineering Design Symposium, SIEDS 2020 |
| DOI |
http://dx.doi.org/10.1109/SIEDS49339.2020.9106583 |
| Abstract |
Flooding events are expected to increase due to climate change. Because of this, cities across the country need to implement flood mitigation strategies in order to ensure the safety and health of their residents. These cities need improved modeling and sensing capabilities to determine which areas (streets, residential neighborhoods, etc.) are flooding in real-time or are vulnerable to flooding from extreme weather events. Both an objective way to monitor stormwater structures and a methodology to rank such structures in accordance to maintenance needs would be valuable. To rank storm structures by peak flow, the methodology consists of using geographic information system (GIS) data combined with Arc Hydro tools to calculate the peak flow of inlet structures grouped by diameter via the rational method. The sensing system is an optical sensor that communicates using LoRa to a The Things Network node. A virtual machine running a Python script extracts the data from The Things Network and places it in an SQLite3 database that can be used for visualization and analysis by decision-makers. Both the GIS-based stormwater infrastructure assessment methodology and flood sensor system are demonstrated using neighborhoods in the City of Charlottesville as a case study. © 2020 IEEE. |
| Author Keywords |
Environmental Monitoring; Internet of Things; LoRa; Smart City |
