When flood waters rise, more data helps better predict and monitor changing conditions. Soon there will be an app for that.
The National Science Foundation (NSF) awarded a Smart and Connected Communities $1.5 million grant to a team of computer scientists and hydrologists from Arizona State University, Michigan Technological University, Northern Arizona University, University of Arizona and University of Buffalo.
The team’s goal is to develop an integrated network that gathers, analyzes and alerts urban communities in danger of flooding.
The system is called the Integrated Flood Stage Observation Network (IFSON) and can take crowdsourced flood data, like smartphone photos, webcams and social media posts, then use image processing to assess flood stage and potential damage.
Later versions of the app will use machine learning techniques. With those risks identified, IFSON can communicate flood info to first responders in real-time. The platform will be adaptable for different neighborhoods and communities.
“We plan on developing several novel technologies for real-time flood monitoring for integration with webcams with image recognition, social media mining, smart phone apps and calibration tools,” says Mikhail Chester, principal investigator for the NSF grant and associate professor in the School of Sustainable Engineering and the Built Environment at Arizona State University. “The prevalence of smart phones and webcams create new opportunities that haven’t existed to provide insights into what’s happening in the world around us. We want to put those tools in the hands of communities to protect themselves against extreme events.
Making the World Better
The IFSON system will be developed in four phases, each focusing on developing a key technology—notably device-based apps—and integrating the platforms to swiftly calculate risk. Outreach is an important step and includes supporting undergraduate student research, and connecting with first responders, sustainability networks and current practitioners in city management, as well as public outreach event organizers. Overall, the project can also improve scientific understanding of how urban landscapes, paved surfaces, drainage engineering and city infrastructure impacts flooding.
Photos taken over time and from many locations can calculate changing water levels and the extent of a flood, which has been the focus of past app development. The new version of the project will use machine learning that adapts algorithms over time as the system is exposed to more and more data and enhance the app’s risk calculations, shifting from a reactive monitoring platform to a proactive predictive platform.
Robert Pastel, associate professor of computer science and affiliated associate professor of cognitive and learning sciences at Michigan Tech, is one of the developers working on IFSON.
“More data means we can answer important questions,” Pastel says. “When does flooding occur, and where, and how much?”
Was this article valuable?
Here are more articles you may enjoy.