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A trans-disciplinary review of deep learning research for water
  resources scientists

A trans-disciplinary review of deep learning research for water resources scientists

6 December 2017
Chaopeng Shen
    AI4CE
ArXivPDFHTML

Papers citing "A trans-disciplinary review of deep learning research for water resources scientists"

22 / 22 papers shown
Title
A Mass-Conserving-Perceptron for Machine Learning-Based Modeling of
  Geoscientific Systems
A Mass-Conserving-Perceptron for Machine Learning-Based Modeling of Geoscientific Systems
Yuan-Heng Wang
Hoshin V. Gupta
AI4CE
32
6
0
12 Oct 2023
Forecasting Soil Moisture Using Domain Inspired Temporal Graph
  Convolution Neural Networks To Guide Sustainable Crop Management
Forecasting Soil Moisture Using Domain Inspired Temporal Graph Convolution Neural Networks To Guide Sustainable Crop Management
Muneeza Azmat
Malvern Madondo
Kelsey L. DiPietro
R. Horesh
Arun Bawa
Michael Jacobs
Raghavan Srinivasan
Fearghal O'Donncha
14
4
0
12 Dec 2022
Towards Daily High-resolution Inundation Observations using Deep
  Learning and EO
Towards Daily High-resolution Inundation Observations using Deep Learning and EO
A. Dasgupta
Lasse Hybbeneth
B. Waske
AI4CE
25
2
0
10 Aug 2022
A review of machine learning concepts and methods for addressing
  challenges in probabilistic hydrological post-processing and forecasting
A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting
Georgia Papacharalampous
Hristos Tyralis
AI4CE
27
28
0
17 Jun 2022
Bathymetry Inversion using a Deep-Learning-Based Surrogate for Shallow
  Water Equations Solvers
Bathymetry Inversion using a Deep-Learning-Based Surrogate for Shallow Water Equations Solvers
Xiaofeng Liu
Yalan Song
Chaopeng Shen
AI4CE
20
9
0
05 Mar 2022
Investigating the fidelity of explainable artificial intelligence
  methods for applications of convolutional neural networks in geoscience
Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience
Antonios Mamalakis
E. Barnes
I. Ebert‐Uphoff
19
73
0
07 Feb 2022
Use of 1D-CNN for input data size reduction of LSTM in Hourly
  Rainfall-Runoff modeling
Use of 1D-CNN for input data size reduction of LSTM in Hourly Rainfall-Runoff modeling
K. Ishida
A. Ercan
T. Nagasato
M. Kiyama
Motoki Amagasaki
AI4TS
14
2
0
07 Nov 2021
Machine Learning for Postprocessing Ensemble Streamflow Forecasts
Machine Learning for Postprocessing Ensemble Streamflow Forecasts
Sanjib Sharma
G. Ghimire
Ridwan Siddique
AI4Cl
11
13
0
15 Jun 2021
Capabilities of Deep Learning Models on Learning Physical Relationships:
  Case of Rainfall-Runoff Modeling with LSTM
Capabilities of Deep Learning Models on Learning Physical Relationships: Case of Rainfall-Runoff Modeling with LSTM
Kazuki Yokoo
K. Ishida
A. Ercan
T. Tu
T. Nagasato
M. Kiyama
Motoki Amagasaki
14
33
0
15 Jun 2021
Neural Network Attribution Methods for Problems in Geoscience: A Novel
  Synthetic Benchmark Dataset
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset
Antonios Mamalakis
I. Ebert‐Uphoff
E. Barnes
OOD
25
75
0
18 Mar 2021
Continental-scale streamflow modeling of basins with reservoirs: towards
  a coherent deep-learning-based strategy
Continental-scale streamflow modeling of basins with reservoirs: towards a coherent deep-learning-based strategy
Wenyu Ouyang
K. Lawson
D. Feng
L. Ye
Chi Zhang
Chaopeng Shen
AI4TS
AI4CE
54
60
0
12 Jan 2021
Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta
  Transfer Learning
Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning
J. Willard
J. Read
A. Appling
S. Oliver
X. Jia
Vipin Kumar
AI4TS
14
55
0
10 Nov 2020
70 years of machine learning in geoscience in review
70 years of machine learning in geoscience in review
Jesper Sören Dramsch
VLM
AI4CE
32
160
0
16 Jun 2020
Deep Learning of Subsurface Flow via Theory-guided Neural Network
Deep Learning of Subsurface Flow via Theory-guided Neural Network
Nanzhe Wang
Dongxiao Zhang
Haibin Chang
Heng Li
AI4CE
25
226
0
24 Oct 2019
Deep convolutional encoder-decoder networks for uncertainty
  quantification of dynamic multiphase flow in heterogeneous media
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
S. Mo
Yinhao Zhu
N. Zabaras
Xiaoqing Shi
Jichun Wu
AI4CE
14
272
0
02 Jul 2018
Methods for Interpreting and Understanding Deep Neural Networks
Methods for Interpreting and Understanding Deep Neural Networks
G. Montavon
Wojciech Samek
K. Müller
FaML
234
2,238
0
24 Jun 2017
MoleculeNet: A Benchmark for Molecular Machine Learning
MoleculeNet: A Benchmark for Molecular Machine Learning
Zhenqin Wu
Bharath Ramsundar
Evan N. Feinberg
Joseph Gomes
C. Geniesse
Aneesh S. Pappu
K. Leswing
Vijay S. Pande
OOD
172
1,778
0
02 Mar 2017
C-RNN-GAN: Continuous recurrent neural networks with adversarial
  training
C-RNN-GAN: Continuous recurrent neural networks with adversarial training
Olof Mogren
GAN
77
512
0
29 Nov 2016
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment
  using Deep Neural Networks
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks
Evan Racah
Seyoon Ko
Peter Sadowski
W. Bhimji
C. Tull
Sang-Yun Oh
Pierre Baldi
P. Prabhat
32
32
0
28 Jan 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,136
0
06 Jun 2015
Improving neural networks by preventing co-adaptation of feature
  detectors
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton
Nitish Srivastava
A. Krizhevsky
Ilya Sutskever
Ruslan Salakhutdinov
VLM
266
7,634
0
03 Jul 2012
MCMC using Hamiltonian dynamics
MCMC using Hamiltonian dynamics
Radford M. Neal
185
3,262
0
09 Jun 2012
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