Chaopeng Shen | DeepAI

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Chi Zhang
179 publications
Li Li
178 publications
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Xiaofeng Liu
59 publications
Daniel Kifer
46 publications
David S. Matteson
32 publications
Lan Wang
26 publications
Yi Zheng
22 publications
Chris Rackauckas
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Wei Ren
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Pierre Gentine
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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...

0 Jiangtao Liu, et al. ∙

∙ 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...

0 Chaopeng Shen, et al. ∙

∙ 11/12/2022

ThreshNet: Segmentation Refinement Inspired by Region-Specific Thresholding

We present ThreshNet, a post-processing method to refine the output of n...

0 Savinay Nagendra, et al. ∙

∙ 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...

5 Dapeng Feng, et al. ∙

∙ 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...

7 Xiaofeng Liu, et al. ∙

∙ 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...

17 Yalan Song, et al. ∙

∙ 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...

0 Judy P. Che-Castaldo, et al. ∙

∙ 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...

9 Wenyu Ouyang, et al. ∙

∙ 01/06/2021

The data synergy effects of time-series deep learning models in hydrology

When fitting statistical models to variables in geoscientific discipline...

6 Kuai Fang, et al. ∙

∙ 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...

6 Dapeng Feng, et al. ∙

∙ 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...

0 Wen-Ping Tsai, et al. ∙

∙ 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...

18 Dapeng Feng, et al. ∙

∙ 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...

1 Kuai Fang, et al. ∙

∙ 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,...

0 Chaopeng Shen, et al. ∙

∙ 07/20/2017

Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network

The Soil Moisture Active Passive (SMAP) mission has delivered valuable s...

0 Kuai Fang, et al. ∙

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