466 citations · 564 across the 7 of their papers we have counts for
7 papers
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Chaopeng Shen, Alison P. Appling, Pierre Gentine +27
Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powe…
Modeling Reservoir Release Using Pseudo-Prospective Learning and Physical Simulations to Predict Water Temperature
Xiaowei Jia, Shengyu Chen, Yiqun Xie +4
This paper proposes a new data-driven method for predicting water temperature in stream networks with reservoirs. The water flows released from reservoirs greatly affect the water…
Heterogeneous Stream-reservoir Graph Networks with Data Assimilation
Shengyu Chen, Alison Appling, Samantha Oliver +5
Accurate prediction of water temperature in streams is critical for monitoring and understanding biogeochemical and ecological processes in streams. Stream temperature is affected…
Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning
Jared D. Willard, Jordan S. Read, Alison P. Appling +3
Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitor…
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks
Xiaowei Jia, Beiyu Lin, Jacob Zwart +4
Effective training of advanced ML models requires large amounts of labeled data, which is often scarce in scientific problems given the substantial human labor and material cost to…
Physics-Guided Recurrent Graph Networks for Predicting Flow and Temperature in River Networks
Xiaowei Jia, Jacob Zwart, Jeffrey Sadler +8
This paper proposes a physics-guided machine learning approach that combines advanced machine learning models and physics-based models to improve the prediction of water flow and t…