Alaska Patent of the Month – March 2022
When conducting research on hydrological prediction systems, the continuity and accuracy of streamflow gauge data is critical. Any break in this data can impact or reduce the effectiveness of decision-making in flood forecasting and reduction activities during flood events. Streamflow data is transferred in near real-time directly to the National Weather Service (NWS) amongst others. This data can include water elevation, river velocity and depth, and more which can predict inflows or potential floods. Any missing data can have large downstream impacts, missing the prediction of a flood.
Dr. Tim Petty with the University of Alaska has recently developed a system which generates synthetic streamflow data which can fill in the gaps if any streamgage malfunctions. While we can do as much as possible to prevent malfunction and safeguard the data that needs to be sent, there is no way to guarantee functionality at all times.
This system receives measured streamflow data from multiple target sources and uses it to classify the streamflow sources into separate groups. Based on these groups, synthetic streamflow data can be generated using a predictive model and regression analyses. The predictive model is trained using historical streamflow data. The model then identifies clusters of similar streamgages which are used as separate groups within the regression analysis. The predicted streamflow data that is generated can then be used to fill any gaps in data during flood prediction and analysis.
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