Flood Informatics: Real Time Flood Level Detection from Multimedia Images and Weather Data

Abstract:

Successive flooding across the U.S continues to pose significant challenges for authorities to smartly control the event and promptly communicate vital information to all parties concerned. Detecting roadway segments inundated due to floodwater has important applications for vehicle routing and traffic management decisions. This study introduces a new flood informatics for rapid detection of flood depth and areas across road networks that are crucial factors for real-time transportation system control. We combined crowdsourced data, satellite imagery, National Weather Service real time forecast with USGS data to provide the intelligence for flood detection model. A deep computing system was developed to detect road flooding and compute flood level and inundation areas.The model incrementally updates its prediction with every new input stream. Our method is vastly experimentally evaluated on synthetic and real world data. We focus on mixed urban and rural road networks of the state of South Carolina as a case study.