During flood hazard events, high water and debris in streams often damage streamgages, which then stop transmitting near-real time data. The loss of this data handicaps decision-making at critical points of a flood event. SHEM builds estimated datasets on the present and historical streamflow data alone through a machine learning process. In an event, when one streamgage stops sending data, SHEM utilizes the historical data that it has already gathered and learned from, to re-build the streamflow dataset on the damaged or non-performing gage. Using this method, lost data can be accurately estimated and then re-applied to fill those data breaks and continue sending critical data to emergency responders.
The Market: Insurance companies and federal government, state government and emergency management.