Machine learning algorithms do a lot for us every day—send unwanted email to our spam folder, warn us if our car is about to back into something and give us recommendations on what TV show to watch next. Now, we are increasingly using these same algorithms make environmental predictions for us.
A team of researchers from the University of Pittsburgh, University of Minnesota and U.S. Geological Survey recently published a new study on predicting flow and temperature in river networks in the 2021 Society for Industrial and Applied Mathematics International Conference on Data Mining proceedings.
The research demonstrates a new machine learning method where the algorithm is taught the “rules” of the physical world in order to make better predictions and steer the algorithm towards physically meaningful relationships between inputs and outputs.
The study presents a model that can make more accurate stream temperature predictions, even when we have little data available, which is the case in most streams. The model can also better generalize to different time periods.
“Water temperature in streams is a ‘master variable’ for many important aquatic systems, including the suitability of aquatic habitats, evaporation rates, greenhouse gas exchange and efficiency of thermoelectric energy production,” said Xiaowei Jia, a lead author of the study and assistant professor in Pitt’s Department of Computer Science in the School of Computing and Information. “Accurate prediction of water temperature and streamflow also aids in decision making for resource managers, for example helping them to determine when and how much water to release from reservoirs to downstream rivers.”