1.1k citations · 3.6k across the 15 of their papers we have counts for
15 papers
Probing the limit of hydrologic predictability with the Transformer network
Jiangtao Liu, Yuchen Bian, Chaopeng Shen
For a number of years since its introduction to hydrology, recurrent neural networks like long short-term memory (LSTM) have proven remarkably difficult to surpass in terms of dail…
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…
Bathymetry Inversion using a Deep-Learning-Based Surrogate for Shallow Water Equations Solvers
Xiaofeng Liu, Yalan Song, Chaopeng Shen
River bathymetry is critical for many aspects of water resources management. We propose and demonstrate a bathymetry inversion method using a deep-learning-based surrogate for shal…
A Robust Statistical Analysis of the Role of Hydropower on the System Electricity Price and Price Volatility
Olukunle O. Owolabi, Kathryn Lawson, Sanhita Sengupta +5
Hydroelectric power (hydropower) is unique in that it can function as both a conventional source of electricity and as backup storage (pumped hydroelectric storage) for providing e…
Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy
Dapeng Feng, Jiangtao Liu, Kathryn Lawson +1
Predictions of hydrologic variables across the entire water cycle have significant value for water resource management as well as downstream applications such as ecosystem and wate…
Surrogate Model for Shallow Water Equations Solvers with Deep Learning
Yalan Song, Chaopeng Shen, Xiaofeng Liu
Shallow water equations are the foundation of most models for flooding and river hydraulics analysis. These physics-based models are usually expensive and slow to run, thus not sui…