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1903.07903
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NeuralHydrology -- Interpreting LSTMs in Hydrology
19 March 2019
Frederik Kratzert
M. Herrnegger
D. Klotz
Sepp Hochreiter
Günter Klambauer
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Papers citing
"NeuralHydrology -- Interpreting LSTMs in Hydrology"
14 / 14 papers shown
Implementing a GRU Neural Network for Flood Prediction in Ashland City, Tennessee
George K. Fordjour
A. Kalyanapu
170
2
0
16 May 2024
Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources
Environmental Data Science (EDS), 2023
J. Willard
C. Varadharajan
X. Jia
Vipin Kumar
AI4TS
274
19
0
18 Aug 2023
Inferring the past: a combined CNN-LSTM deep learning framework to fuse satellites for historical inundation mapping
J. Giezendanner
Rohit Mukherjee
Matthew Purri
Mitchell Thomas
Max Mauerman
A. Islam
B. Tellman
151
11
0
01 May 2023
Explainable AI for Time Series via Virtual Inspection Layers
Pattern Recognition (Pattern Recogn.), 2023
Johanna Vielhaben
Sebastian Lapuschkin
G. Montavon
Wojciech Samek
XAI
AI4TS
239
42
0
11 Mar 2023
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022
Pattarawat Chormai
J. Herrmann
Klaus-Robert Muller
G. Montavon
FAtt
438
29
0
30 Dec 2022
Imputation of Missing Streamflow Data at Multiple Gauging Stations in Benin Republic
R. Mbuvha
J. Adounkpe
W. Mongwe
M. Houngnibo
N. Newlands
T. Marwala
44
2
0
17 Nov 2022
Few-Shot Learning by Dimensionality Reduction in Gradient Space
M. Gauch
M. Beck
Thomas Adler
D. Kotsur
Stefan Fiel
...
Markus Holzleitner
Werner Zellinger
D. Klotz
Sepp Hochreiter
Sebastian Lehner
191
11
0
07 Jun 2022
Toward Explainable AI for Regression Models
IEEE Signal Processing Magazine (IEEE SPM), 2021
S. Letzgus
Patrick Wagner
Jonas Lederer
Wojciech Samek
Klaus-Robert Muller
G. Montavon
XAI
234
83
0
21 Dec 2021
Transfer learning to improve streamflow forecasts in data sparse regions
R. Oruche
Lisa Egede
T. Baker
Fearghal O'Donncha
AI4TS
144
14
0
06 Dec 2021
Capabilities of Deep Learning Models on Learning Physical Relationships: Case of Rainfall-Runoff Modeling with LSTM
Science of the Total Environment (Sci. Total Environ.), 2021
Kazuki Yokoo
K. Ishida
A. Ercan
T. Tu
T. Nagasato
M. Kiyama
Motoki Amagasaki
244
46
0
15 Jun 2021
MC-LSTM: Mass-Conserving LSTM
International Conference on Machine Learning (ICML), 2021
Pieter-Jan Hoedt
Frederik Kratzert
D. Klotz
Christina Halmich
Markus Holzleitner
G. Nearing
Sepp Hochreiter
Günter Klambauer
254
70
0
13 Jan 2021
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Wojciech Samek
G. Montavon
Sebastian Lapuschkin
Christopher J. Anders
K. Müller
XAI
401
87
0
17 Mar 2020
Enhancing streamflow forecast and extracting insights using long-short term memory networks with data integration at continental scales
Water Resources Research (WRR), 2019
D. Feng
K. Fang
Chaopeng Shen
AI4TS
235
349
0
18 Dec 2019
Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasets
Hydrology and Earth System Sciences (HESS), 2019
Frederik Kratzert
D. Klotz
Guy Shalev
Günter Klambauer
Sepp Hochreiter
G. Nearing
271
713
0
19 Jul 2019
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