Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

NeuralHydrology — A Python library for Deep Learning research in hydrology
Frederik Kratzert, Martin Gauch, Grey Nearing, et al.
The Journal of Open Source Software (2022) Vol. 7, Iss. 71, pp. 4050-4050
Open Access | Times Cited: 136

Showing 1-25 of 136 citing articles:

Deep learning rainfall–runoff predictions of extreme events
Jonathan Frame, Frederik Kratzert, Daniel Klotz, et al.
Hydrology and earth system sciences (2022) Vol. 26, Iss. 13, pp. 3377-3392
Open Access | Times Cited: 204
Hydrological concept formation inside long short-term memory (LSTM) networks
Thomas Lees, Steven Reece, Frederik Kratzert, et al.
Hydrology and earth system sciences (2022) Vol. 26, Iss. 12, pp. 3079-3101
Open Access | Times Cited: 127
Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models
Richard Arsenault, Jean‐Luc Martel, Frédéric Brunet, et al.
Hydrology and earth system sciences (2023) Vol. 27, Iss. 1, pp. 139-157
Open Access | Times Cited: 102
The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL)
Juliane Mai, Hongren Shen, Bryan A. Tolson, et al.
Hydrology and earth system sciences (2022) Vol. 26, Iss. 13, pp. 3537-3572
Open Access | Times Cited: 78
Global prediction of extreme floods in ungauged watersheds
Grey Nearing, Déborah Cohen, Vusumuzi Dube, et al.
Nature (2024) Vol. 627, Iss. 8004, pp. 559-563
Open Access | Times Cited: 73
HESS Opinions: Never train an LSTM on a single basin
Frederik Kratzert, Martin Gauch, Daniel Klotz, et al.
(2024)
Open Access | Times Cited: 35
HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin
Frederik Kratzert, Martin Gauch, Daniel Klotz, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 17, pp. 4187-4201
Open Access | Times Cited: 16
Predicting streamflow with LSTM networks using global datasets
Katharina Wilbrand, Riccardo Taormina, Marie‐Claire ten Veldhuis, et al.
Frontiers in Water (2023) Vol. 5
Open Access | Times Cited: 22
Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework
Tom Botterill, Hilary McMillan
Water Resources Research (2023) Vol. 59, Iss. 3
Open Access | Times Cited: 21
Toward interpretable LSTM-based modeling of hydrological systems
Luis De La Fuente, Mohammad Reza Ehsani, Hoshin V. Gupta, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 4, pp. 945-971
Open Access | Times Cited: 10
Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia
Stephanie Clark, Julien Lerat, Jean‐Michel Perraud, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 5, pp. 1191-1213
Open Access | Times Cited: 9
Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach
Qiutong Yu, Bryan A. Tolson, Hongren Shen, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 9, pp. 2107-2122
Open Access | Times Cited: 7
HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network
Van Tam Nguyen, Vinh Ngoc Tran, Hoang Tran, et al.
Ecological Informatics (2025) Vol. 85, pp. 102994-102994
Open Access
DeepBase: A Deep Learning-based Daily Baseflow Dataset across the United States
Parnian Ghaneei, Hamid Moradkhani
Scientific Data (2025) Vol. 12, Iss. 1
Open Access
Improved streamflow simulations in hydrologically diverse basins using physically informed deep learning models
Bhanu Magotra, Manabendra Saharia, C. T. Dhanya
Hydrological Sciences Journal (2025)
Closed Access
Machine Learning-Based Reconstruction and Prediction of Groundwater Time Series in the Allertal, Germany
Tuong Vi Tran, Aaron Peche, Robert Kringel, et al.
Water (2025) Vol. 17, Iss. 3, pp. 433-433
Open Access
Data-driven model as a post-process for daily streamflow prediction in ungauged basins
Jeonghyeon Choi, Sangdan Kim
Heliyon (2025) Vol. 11, Iss. 4, pp. e42512-e42512
Open Access
Are LSTM and conceptual rainfall-runoff models able to cope with limited training datasets under diverse hydrometeorological conditions?
Fadil Boodoo, Renaud Hostache, Nadia Skifa, et al.
Modeling Earth Systems and Environment (2025) Vol. 11, Iss. 2
Closed Access
Improving multi-model ensemble streamflow forecasts by combining lumped, distributed and deep learning hydrological models
William F. Armstrong, Richard Arsenault, Jean‐Luc Martel, et al.
Hydrological Sciences Journal (2025)
Closed Access
The multi-GPU Wetland DEM Ponding Model
Tonghe Liu, S. J. Trim, Seok‐Bum Ko, et al.
Computers & Geosciences (2025), pp. 105912-105912
Closed Access
Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events
Eduardo Acuña Espinoza, Ralf Loritz, Frederik Kratzert, et al.
Hydrology and earth system sciences (2025) Vol. 29, Iss. 5, pp. 1277-1294
Open Access
Are Deep Learning Models in Hydrology Entity Aware?
Benedikt Heudorfer, Hoshin V. Gupta, Ralf Loritz
Geophysical Research Letters (2025) Vol. 52, Iss. 6
Open Access
Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks
Grey Nearing, Daniel Klotz, Jonathan Frame, et al.
Hydrology and earth system sciences (2022) Vol. 26, Iss. 21, pp. 5493-5513
Open Access | Times Cited: 24
Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models
Louise Slater, Louise Arnal, Marie‐Amélie Boucher, et al.
(2022)
Open Access | Times Cited: 23
A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks
Jun Liu, Julian Koch, Simon Stisen, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 13, pp. 2871-2893
Open Access | Times Cited: 4
Page 1 - Next Page

Scroll to top