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06/21/2023
Probing the limit of hydrologic predictability with the Transformer network
For a number of years since its introduction to hydrology, recurrent neu...
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01/10/2023
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Process-Based Modeling (PBM) and Machine Learning (ML) are often perceiv...
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11/12/2022
ThreshNet: Segmentation Refinement Inspired by Region-Specific Thresholding
We present ThreshNet, a post-processing method to refine the output of n...
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03/28/2022
Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy
Predictions of hydrologic variables across the entire water cycle have s...
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03/05/2022
Bathymetry Inversion using a Deep-Learning-Based Surrogate for Shallow Water Equations Solvers
River bathymetry is critical for many aspects of water resources managem...
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12/20/2021
Surrogate Model for Shallow Water Equations Solvers with Deep Learning
Shallow water equations are the foundation of most models for flooding a...
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01/19/2021
Critical Risk Indicators (CRIs) for the electric power grid: A survey and discussion of interconnected effects
The electric power grid is a critical societal resource connecting multi...
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01/12/2021
Continental-scale streamflow modeling of basins with reservoirs: a demonstration of effectiveness and a delineation of challenges
A large fraction of major waterways have dams influencing streamflow, wh...
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01/06/2021
The data synergy effects of time-series deep learning models in hydrology
When fitting statistical models to variables in geoscientific discipline...
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11/26/2020
Prediction in ungauged regions with sparse flow duration curves and input-selection ensemble modeling
While long short-term memory (LSTM) models have demonstrated stellar per...
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07/30/2020
From parameter calibration to parameter learning: Revolutionizing large-scale geoscientific modeling with big data
The behaviors and skills of models in many geoscientific domains strongl...
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12/18/2019
Enhancing streamflow forecast and extracting insights using long-short term memory networks with data integration at continental scales
Recent observations with varied schedules and types (moving average, sna...
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06/10/2019
Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions
Soil moisture is an important variable that determines floods, vegetatio...
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12/06/2017
A trans-disciplinary review of deep learning research for water resources scientists
Deep learning (DL), a new-generation artificial neural network research,...
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07/20/2017