Please use this zoom link for the event.
Please join us for a UW Data Science Seminar event on Thursday, January 21st from 4:30 to 5:30 p.m. with Andrew Bennett, PhD candidate, Civil & Environmental Engineering.
“Embedding neural networks into large Earth systems models”
Deep learning (DL) methods have shown great promise for accurately predicting hydrologic processes but have not yet reached the complexity of traditional process-based hydrologic models (PBHM). While DL methods have been able to achieve superior predictive performance for specific tasks, the ability of PBHMs to simulate the entire hydrologic cycle makes them useful for a wide range of modeling and simulation tasks. We take advantage of both of these approaches by coupling a DL model into a PBHM as process parameterizations for the simulation of evaporation and heat transfer. In this seminar we will describe the workflow and technologies needed to perform this coupling, as well as provide an outlook for the future of such applications. Our results demonstrate that the DL parameterizations can outperform physics-based equations for evaporation and heat transfer in several ways. We show that the DL parameterizations show improvements in predictive performance as well as provide more realistic simulations of other aspects which were not directly trained for by taking advantage of information from other model components. Our work demonstrates how combining modeling approaches can lead to better models.
The UW Data Science Seminar is an annual lecture series at the University of Washington that hosts scholars working across applied areas of data science, such as the sciences, engineering, humanities and arts along with methodological areas in data science, such as computer science, applied math and statistics. Our presenters come from all domain fields and include occasional external speakers from regional partners, governmental agencies and industry.
All seminars will be hosted virtually for the 2020-2021 academic year, and are free and open to the public.