A new computer model uses a better artificial intelligence process to measure snow and water availability more accurately across vast distances in the West, information that could someday be used to better predict water availability for farmers and others.
A new computer model uses a better artificial intelligence process to measure snow and water availability more accurately across vast distances in the West, information that could someday be used to better predict water availability for farmers and others.
Publishing in the Proceedings of the AAAI Conference on Artificial Intelligence, the interdisciplinary group of Washington State University researchers predict water availability from areas in the West where snow amounts aren’t being physically measured.
Comparing their results to measurements from more than 300 snow measuring stations in the Western U.S., they showed that their model outperformed other models that use the AI process known as machine learning. Previous models focused on time-related measures, taking data at different time points from only a few locations. The improved model uses both time and space into account, resulting in more accurate predictions.
The information is critically important for water planners throughout the West because “every drop of water” is appropriated for irrigation, hydropower, drinking water, and environmental needs, said Krishu Thapa, a Washington State University computer science graduate student who led the study.
Read more at Washington State University
Image: Mt. Eyak SNOTEL site, above the coastal town of Cordova, Alaska. Snow depth is about 10.5 feet, 45% density. Taken April 2012. Photo by Daniel Fisher of the USDA Natural Resources Conservation Service.
