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OpenAlex Citation Counts

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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:

What Role Does Hydrological Science Play in the Age of Machine Learning?
Grey Nearing, Frederik Kratzert, Alden Keefe Sampson, et al.
EarthArXiv (California Digital Library) (2020)
Open Access | Times Cited: 158

Showing 1-25 of 158 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
Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models
Thomas Lees, Marcus Buechel, Bailey Anderson, et al.
Hydrology and earth system sciences (2021) Vol. 25, Iss. 10, pp. 5517-5534
Open Access | Times Cited: 164
Uncertainty estimation with deep learning for rainfall–runoff modeling
Daniel Klotz, Frederik Kratzert, Martin Gauch, et al.
Hydrology and earth system sciences (2022) Vol. 26, Iss. 6, pp. 1673-1693
Open Access | Times Cited: 132
Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling
Saman Razavi
Environmental Modelling & Software (2021) Vol. 144, pp. 105159-105159
Open Access | Times Cited: 119
Flood forecasting with machine learning models in an operational framework
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal, et al.
Hydrology and earth system sciences (2022) Vol. 26, Iss. 15, pp. 4013-4032
Open Access | Times Cited: 116
Prediction of estuarine water quality using interpretable machine learning approach
Shuo Wang, Hui Peng, Shengkang Liang
Journal of Hydrology (2021) Vol. 605, pp. 127320-127320
Closed Access | Times Cited: 114
LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe
Christoph Klingler, Karsten Schulz, Mathew Herrnegger
Earth system science data (2021) Vol. 13, Iss. 9, pp. 4529-4565
Open Access | Times Cited: 112
Simulation of regional groundwater levels in arid regions using interpretable machine learning models
Qi Liu, Dongwei GUI, Lei Zhang, et al.
The Science of The Total Environment (2022) Vol. 831, pp. 154902-154902
Closed Access | Times Cited: 78
HESS Opinions: Never train an LSTM on a single basin
Frederik Kratzert, Martin Gauch, Daniel Klotz, et al.
(2024)
Open Access | Times Cited: 35
Deep learning, hydrological processes and the uniqueness of place
Keith Beven
Hydrological Processes (2020) Vol. 34, Iss. 16, pp. 3608-3613
Open Access | Times Cited: 120
On doing hydrology with dragons: Realizing the value of perceptual models and knowledge accumulation
Thorsten Wagener, Tom Gleeson, Gemma Coxon, et al.
Wiley Interdisciplinary Reviews Water (2021) Vol. 8, Iss. 6
Open Access | Times Cited: 66
A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations
Anna E. Sikorska‐Senoner, John Quilty
Environmental Modelling & Software (2021) Vol. 143, pp. 105094-105094
Open Access | Times Cited: 63
On Strictly Enforced Mass Conservation Constraints for Modeling the Rainfall-Runoff Process
Jonathan Frame, Paul Ullrich, Grey Nearing, et al.
EarthArXiv (California Digital Library) (2022)
Open Access | Times Cited: 44
On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process
Jonathan Frame, Frederik Kratzert, Hoshin V. Gupta, et al.
Hydrological Processes (2023) Vol. 37, Iss. 3
Closed Access | Times Cited: 27
Long-term Reservoir Inflow Forecasts: Enhanced Water Supply and Inflow Volume Accuracy Using Deep Learning
Zachary C. Herbert, Zeeshan Asghar, Carlos Oroza
Journal of Hydrology (2021) Vol. 601, pp. 126676-126676
Open Access | Times Cited: 47
Surface water temperature prediction in large-deep reservoirs using a long short-term memory model
Longfan Wang, Bo Xu, Chi Zhang, et al.
Ecological Indicators (2021) Vol. 134, pp. 108491-108491
Open Access | Times Cited: 41
Regionalization in a Global Hydrologic Deep Learning Model: From Physical Descriptors to Random Vectors
Xiang Li, Ankush Khandelwal, Xiaowei Jia, et al.
Water Resources Research (2022) Vol. 58, Iss. 8
Open Access | Times Cited: 35
Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling
Daniel Klotz, Frederik Kratzert, Martin Gauch, et al.
EarthArXiv (California Digital Library) (2020)
Open Access | Times Cited: 49
Emerging technologies and radical collaboration to advance predictive understanding of watershed hydrobiogeochemistry
Susan S. Hubbard, Charuleka Varadharajan, Yuxin Wu, et al.
Hydrological Processes (2020) Vol. 34, Iss. 15, pp. 3175-3182
Open Access | Times Cited: 46
Improving hydrologic models for predictions and process understanding using Neural ODEs
Marvin Höge, Andreas Scheidegger, Marco Baity‐Jesi, et al.
(2022)
Open Access | Times Cited: 24
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 Multilayer Perceptron Model for Stochastic Synthesis
Evangelos Rozos, Panayiotis Dimitriadis, Katerina Mazi, et al.
Hydrology (2021) Vol. 8, Iss. 2, pp. 67-67
Open Access | Times Cited: 31
Addressing hydrological modeling in watersheds under land cover change with deep learning
Daniel Althoff, Lineu Neiva Rodrigues, Demétrius David da Silva
Advances in Water Resources (2021) Vol. 154, pp. 103965-103965
Closed Access | Times Cited: 31
Machine Learning for Understanding Inland Water Quantity, Quality, and Ecology
Alison Appling, Samantha K. Oliver, Jordan S. Read, et al.
Elsevier eBooks (2022), pp. 585-606
Open Access | Times Cited: 21
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