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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:

HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community
Chaopeng Shen, Eric Laloy, Amin Elshorbagy, et al.
Hydrology and earth system sciences (2018) Vol. 22, Iss. 11, pp. 5639-5656
Open Access | Times Cited: 306

Showing 1-25 of 306 citing articles:

Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks
Frederik Kratzert, Daniel Klotz, Claire Brenner, et al.
Hydrology and earth system sciences (2018) Vol. 22, Iss. 11, pp. 6005-6022
Open Access | Times Cited: 1212
Deep learning in environmental remote sensing: Achievements and challenges
Qiangqiang Yuan, Huanfeng Shen, Tongwen Li, et al.
Remote Sensing of Environment (2020) Vol. 241, pp. 111716-111716
Closed Access | Times Cited: 1177
What Role Does Hydrological Science Play in the Age of Machine Learning?
Grey Nearing, Frederik Kratzert, Alden Keefe Sampson, et al.
Water Resources Research (2020) Vol. 57, Iss. 3
Open Access | Times Cited: 518
How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions
Alexander Y. Sun, Bridget R. Scanlon
Environmental Research Letters (2019) Vol. 14, Iss. 7, pp. 073001-073001
Open Access | Times Cited: 396
A review of the application of machine learning in water quality evaluation
Mengyuan Zhu, Jiawei Wang, Yang Xiao, et al.
Eco-Environment & Health (2022) Vol. 1, Iss. 2, pp. 107-116
Open Access | Times Cited: 375
Process‐Guided Deep Learning Predictions of Lake Water Temperature
Jordan S. Read, Xiaowei Jia, Jared Willard, et al.
Water Resources Research (2019) Vol. 55, Iss. 11, pp. 9173-9190
Open Access | Times Cited: 326
Improving Precipitation Estimation Using Convolutional Neural Network
Baoxiang Pan, Kuolin Hsu, Amir AghaKouchak, et al.
Water Resources Research (2019) Vol. 55, Iss. 3, pp. 2301-2321
Open Access | Times Cited: 235
Ensemble flood forecasting: Current status and future opportunities
Wenyan Wu, Rebecca Emerton, Qingyun Duan, et al.
Wiley Interdisciplinary Reviews Water (2020) Vol. 7, Iss. 3
Open Access | Times Cited: 213
From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale?
Wei Zhi, Dapeng Feng, Wen‐Ping Tsai, et al.
Environmental Science & Technology (2021) Vol. 55, Iss. 4, pp. 2357-2368
Closed Access | Times Cited: 211
From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling
Wen‐Ping Tsai, Dapeng Feng, Ming Pan, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 206
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: 172
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
Best Management Practices for Diffuse Nutrient Pollution: Wicked Problems Across Urban and Agricultural Watersheds
Anna Lintern, Lauren McPhillips, Brandon K. Winfrey, et al.
Environmental Science & Technology (2020) Vol. 54, Iss. 15, pp. 9159-9174
Closed Access | Times Cited: 156
Differentiable modelling to unify machine learning and physical models for geosciences
Chaopeng Shen, Alison P. Appling, Pierre Gentine, et al.
Nature Reviews Earth & Environment (2023) Vol. 4, Iss. 8, pp. 552-567
Closed Access | Times Cited: 155
Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural Perturbations
Samuel N. Araya, Teamrat A. Ghezzehei
Water Resources Research (2019) Vol. 55, Iss. 7, pp. 5715-5737
Open Access | Times Cited: 150
Machine learning for hydrologic sciences: An introductory overview
Tianfang Xu, Feng Liang
Wiley Interdisciplinary Reviews Water (2021) Vol. 8, Iss. 5
Closed Access | Times Cited: 150
Toward catchment hydro‐biogeochemical theories
Li Li, Pamela Sullivan, Paolo Benettin, et al.
Wiley Interdisciplinary Reviews Water (2020) Vol. 8, Iss. 1
Open Access | Times Cited: 149
Forecasting of water level in multiple temperate lakes using machine learning models
Senlin Zhu, Bahrudin Hrnjica, Mariusz Ptak, et al.
Journal of Hydrology (2020) Vol. 585, pp. 124819-124819
Closed Access | Times Cited: 145
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: 135
The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology
Kuai Fang, Daniel Kifer, Kathryn Lawson, et al.
Water Resources Research (2022) Vol. 58, Iss. 4
Open Access | Times Cited: 134
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: 121
Editorial: Broadening the Use of Machine Learning in Hydrology
Chaopeng Shen, Xingyuan Chen, Eric Laloy
Frontiers in Water (2021) Vol. 3
Open Access | Times Cited: 103
The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management
Vijendra Kumar, Hazi Mohammad Azamathulla, Kul Vaibhav Sharma, et al.
Sustainability (2023) Vol. 15, Iss. 13, pp. 10543-10543
Open Access | Times Cited: 100
Towards hybrid modeling of the global hydrological cycle
Basil Kraft, Martin Jung, Marco Körner, et al.
Hydrology and earth system sciences (2022) Vol. 26, Iss. 6, pp. 1579-1614
Open Access | Times Cited: 95
Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions
Kumar Puran Tripathy, Ashok K. Mishra
Journal of Hydrology (2023) Vol. 628, pp. 130458-130458
Closed Access | Times Cited: 87
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