Learning Machines Seminars

Stream temperature forecasts using process-guided deep learning and data assimilation in support of management decisions

Jacob Zwart, U.S. Geological Survey. Stream temperature forecasts using process-guided deep learning and data assimilation in support of management decisions.

Abstract: Multiple and competing water demands can require complex water allocation decisions that must be made with imperfect information about the future. Near-term forecasts of environmental outcomes can inform real-time decision making by providing predicted future conditions with associated uncertainty. In this talk, we describe our approach of combining process-guided deep learning models with a data assimilation algorithm to generate 7-day forecasts of maximum stream water temperature in the Delaware River Basin to support New York City reservoir management decisions. We evaluate the forecast accuracy and reliability during the summer of 2021 and discuss future workflow and model improvements to better deliver valuable information in support of environmental management decisions.

Bio: Jake Zwart (he/him) is a data scientist at the U.S. Geological Survey (USGS) in the Data Science Branch of the Water Mission Area. Jake's work currently focuses on producing short-term forecasts of stream temperature at regional scales to aid in water resources decision making. The data science group at USGS has developed techniques to inject scientific knowledge into machine learning models (process-guided deep learning) to make accurate predictions

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