Urban Flash Flood Monitoring, Mapping and Forecasting via a Tailored Sensor Network System

Heavy convective rainfall often results in flooding in urban regions. The rainfall runoff process, however, is highly complex, nonlinear, and temporally and spatially varying because of the variability of the terrain and climate attributes. An intelligent traffic system covering the emergency response capacity in response to flooding impacts in low-lying area of urban regions is highly desirable to drivers and passengers nowadays. The proposed motes-based sensor network has three major modules, which are water level monitoring module, network video record module, and data processing module. The water level monitoring module sensors water level information and transmits it to a server computer via motes-based wireless network. The network video record module takes and sends video in real-time to the server too. The data processing module linking existing flooding level with NOAA NEXRAD rainfall prediction data enables us to publish online raw data, predict inundation zone, and disseminate video information through different media. Such integration would greatly enhance the disaster management capacity in hurricane events