| Title | Smart Cities Solutions for More Flood Resilient Communities |
|---|---|
| ID_Doc | 43915 |
| Authors | Carlson, K; Chowdhury, A; Kepley, A; Somerville, E; Warshaw, K; Goodall, J |
| Title | Smart Cities Solutions for More Flood Resilient Communities |
| Year | 2019 |
| Published | |
| Abstract | There is evidence that flooding events are becoming more frequent and intense as a result of climate change. This problem is especially prevalent in Norfolk, VA which has the second highest rate of sea level rise on the east coast. Model and sensing innovations are needed to produce high-resolution flood warnings in real-time to improve public safety. New sensing approaches are also needed to accurately measure the extent of flooding during storm events so this data can be used to calibrate models. Our methodology creates an end-to-end modeling system for Norfolk, VA to provide real-time flood forecast information to users. Our process begins with data collection through our group's water level sensor. This device relies on an ultrasonic sensor to measure how its distance from the ground changes as water levels rise. Readings are then filtered before they are transmitted to a persistent database. The data from this sensor, combined with historical flood data, are stored in a locally-hosted relational SQLite database and a cloud-hosted InfluxDB database. The locally-hosted database can be used for further development of flood prediction models. The cloud-hosted database can store data as it is collected for real time analysis. Currently, the sensor has accurately recorded changes in distances of up to ten feet in the lab and successfully transmitted these readings. For future testing, measurements will be sent to a static URL hosted on Heroku. A Python function has been written that reads the URL in JSON format and transmits the data to the Influx database. Another Python function has been written that reads a csv containing historical data and transforms it to the proper format, then inserts it into SQLite. |
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