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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:

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: 118

Showing 1-25 of 118 citing articles:

Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions
Kai Ma, Dapeng Feng, Kathryn Lawson, et al.
Water Resources Research (2021) Vol. 57, Iss. 5
Closed Access | Times Cited: 171
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: 162
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: 134
Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling
Herath Mudiyanselage Viraj Vidura Herath, Jayashree Chadalawada, Vladan Babovic
Hydrology and earth system sciences (2021) Vol. 25, Iss. 8, pp. 4373-4401
Open Access | Times Cited: 128
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
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: 114
A hybrid deep learning algorithm and its application to streamflow prediction
Yongen Lin, Dagang Wang, Guiling Wang, et al.
Journal of Hydrology (2021) Vol. 601, pp. 126636-126636
Closed Access | Times Cited: 109
Hybrid forecasting: blending climate predictions with AI models
Louise Slater, Louise Arnal, Marie‐Amélie Boucher, et al.
Hydrology and earth system sciences (2023) Vol. 27, Iss. 9, pp. 1865-1889
Open Access | Times Cited: 78
Coevolution of machine learning and process‐based modelling to revolutionize Earth and environmental sciences: A perspective
Saman Razavi, David M. Hannah, Amin Elshorbagy, et al.
Hydrological Processes (2022) Vol. 36, Iss. 6
Open Access | Times Cited: 69
Integrating urban water fluxes and moving beyond impervious surface cover: A review
Claire Oswald, Christa Kelleher, Sarah H. Ledford, et al.
Journal of Hydrology (2023) Vol. 618, pp. 129188-129188
Closed Access | Times Cited: 58
Distributed Hydrological Modeling With Physics‐Encoded Deep Learning: A General Framework and Its Application in the Amazon
Chao Wang, Shijie Jiang, Yi Zheng, et al.
Water Resources Research (2024) Vol. 60, Iss. 4
Open Access | Times Cited: 15
GMD perspective: The quest to improve the evaluation of groundwater representation in continental- to global-scale models
Tom Gleeson, Thorsten Wagener, Petra Döll, et al.
Geoscientific model development (2021) Vol. 14, Iss. 12, pp. 7545-7571
Open Access | Times Cited: 78
Quantifying Streamflow Depletion from Groundwater Pumping: A Practical Review of Past and Emerging Approaches for Water Management
Samuel C. Zipper, William Farmer, Andrea E. Brookfield, et al.
JAWRA Journal of the American Water Resources Association (2022) Vol. 58, Iss. 2, pp. 289-312
Open Access | Times Cited: 38
Enhancing streamflow simulation using hybridized machine learning models in a semi-arid basin of the Chinese loess Plateau
Qiang Yu, Liguang Jiang, Yanjun Wang, et al.
Journal of Hydrology (2023) Vol. 617, pp. 129115-129115
Closed Access | Times Cited: 30
Benchmarking hydrological models for an uncertain future
Keith Beven
Hydrological Processes (2023) Vol. 37, Iss. 5
Open Access | Times Cited: 30
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: 26
Perceptual perplexity and parameter parsimony
Keith Beven, Nick A. Chappell
Wiley Interdisciplinary Reviews Water (2021) Vol. 8, Iss. 4
Closed Access | Times Cited: 46
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
On (in)validating environmental models. 1. Principles for formulating a Turing‐like Test for determining when a model is fit‐for purpose
Keith Beven, Stuart N. Lane
Hydrological Processes (2022) Vol. 36, Iss. 10
Open Access | Times Cited: 33
On (in)validating environmental models. 2. Implementation of a Turing‐like test to modelling hydrological processes
Keith Beven, Stuart N. Lane, Trevor Page, et al.
Hydrological Processes (2022) Vol. 36, Iss. 10
Open Access | Times Cited: 27
Streamflow prediction using a hybrid methodology based on variational mode decomposition (VMD) and machine learning approaches
Farshad Ahmadi, Mansour Tohidi, Meysam Sadrianzade
Applied Water Science (2023) Vol. 13, Iss. 6
Open Access | Times Cited: 20
Improvement of streamflow simulation by combining physically hydrological model with deep learning methods in data-scarce glacial river basin
Chengde Yang, Min Xu, Shichang Kang, et al.
Journal of Hydrology (2023) Vol. 625, pp. 129990-129990
Closed Access | Times Cited: 20
Self-training approach to improve the predictability of data-driven rainfall-runoff model in hydrological data-sparse regions
Sung-Hyun Yoon, Kuk‐Hyun Ahn
Journal of Hydrology (2024) Vol. 632, pp. 130862-130862
Closed Access | Times Cited: 6
Unit Operation and Process Modeling with Physics-Informed Machine Learning
Haochen Li, David Spelman, John J. Sansalone
Journal of Environmental Engineering (2024) Vol. 150, Iss. 4
Open Access | Times Cited: 5
Runoff simulation in the upper Han River Basin using physics-informed machine learningmodel
Chao Deng, Sun Peiyuan, Xin Yin, et al.
Journal of Lake Sciences (2025) Vol. 37, Iss. 1, pp. 279-292
Open Access
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