P. Finley, Grayson Gatti, J. Goodall, Mac Nelson, Kiri Nicholson, K. Shah
{"title":"Flood Monitoring and Mitigation Strategies for Flood-Prone Urban Areas","authors":"P. Finley, Grayson Gatti, J. Goodall, Mac Nelson, Kiri Nicholson, K. Shah","doi":"10.1109/SIEDS49339.2020.9106583","DOIUrl":null,"url":null,"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.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Systems and Information Engineering Design Symposium (SIEDS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIEDS49339.2020.9106583","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
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.
由于气候变化,洪水事件预计会增加。因此,全国各城市需要实施防洪战略,以确保居民的安全和健康。这些城市需要改进建模和传感能力,以确定哪些区域(街道、居民区等)正在实时发生洪水,或容易受到极端天气事件的洪水影响。一种客观的方法来监测雨水结构,以及一种根据维修需要对这些结构进行排序的方法,都是很有价值的。采用地理信息系统(GIS)数据与Arc Hydro工具相结合的方法,通过合理的方法计算按直径分组的入口结构的峰值流量。传感系统是一个光学传感器,通过LoRa与The Things Network节点通信。运行Python脚本的虚拟机从the Things Network中提取数据并将其放入SQLite3数据库中,该数据库可用于决策者的可视化和分析。以夏洛茨维尔市的社区为例,展示了基于gis的雨水基础设施评估方法和洪水传感器系统。