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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:

Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning
Shijie Jiang, Yi Zheng, Dimitri Solomatine
Geophysical Research Letters (2020) Vol. 47, Iss. 13
Open Access | Times Cited: 275

Showing 1-25 of 275 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
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
Differentiable modelling to unify machine learning and physical models for geosciences
Chaopeng Shen, Alison Appling, Pierre Gentine, et al.
Nature Reviews Earth & Environment (2023) Vol. 4, Iss. 8, pp. 552-567
Closed Access | Times Cited: 149
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: 148
Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships
Kang Xie, Pan Liu, Jianyun Zhang, et al.
Journal of Hydrology (2021) Vol. 603, pp. 127043-127043
Closed Access | Times Cited: 146
Differentiable, Learnable, Regionalized Process‐Based Models With Multiphysical Outputs can Approach State‐Of‐The‐Art Hydrologic Prediction Accuracy
Dapeng Feng, Jiangtao Liu, Kathryn Lawson, et al.
Water Resources Research (2022) Vol. 58, Iss. 10
Open Access | Times Cited: 146
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
Analysis of runoff generation driving factors based on hydrological model and interpretable machine learning method
Shuo Wang, Hui Peng, Qin Hu, et al.
Journal of Hydrology Regional Studies (2022) Vol. 42, pp. 101139-101139
Open Access | Times Cited: 90
Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change
Sungwook Wi, Scott Steinschneider
Water Resources Research (2022) Vol. 58, Iss. 9
Closed Access | Times Cited: 84
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: 75
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
Deep learning for water quality
Wei Zhi, Alison Appling, Heather E. Golden, et al.
Nature Water (2024) Vol. 2, Iss. 3, pp. 228-241
Closed Access | Times Cited: 55
The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment
Dapeng Feng, Hylke E. Beck, Kathryn Lawson, et al.
Hydrology and earth system sciences (2023) Vol. 27, Iss. 12, pp. 2357-2373
Open Access | Times Cited: 54
Improving LSTM hydrological modeling with spatiotemporal deep learning and multi-task learning: A case study of three mountainous areas on the Tibetan Plateau
Bu Li, Ruidong Li, Ting Sun, et al.
Journal of Hydrology (2023) Vol. 620, pp. 129401-129401
Open Access | Times Cited: 44
Artificial intelligence for geoscience: Progress, challenges and perspectives
Tianjie Zhao, Sheng Wang, Chaojun Ouyang, et al.
The Innovation (2024) Vol. 5, Iss. 5, pp. 100691-100691
Open Access | Times Cited: 40
How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences
Shijie Jiang, Lily‐belle Sweet, Georgios Blougouras, et al.
Earth s Future (2024) Vol. 12, Iss. 7
Open Access | Times Cited: 25
Multiple spatio-temporal scale runoff forecasting and driving mechanism exploration by K-means optimized XGBoost and SHAP
Shuo Wang, Hui Peng
Journal of Hydrology (2024) Vol. 630, pp. 130650-130650
Closed Access | Times Cited: 22
Trustworthy remote sensing interpretation: Concepts, technologies, and applications
Sheng Wang, Wei Han, Xiaohui Huang, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2024) Vol. 209, pp. 150-172
Closed Access | Times Cited: 21
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: 17
Coupling Deep Learning and Physically Based Hydrological Models for Monthly Streamflow Predictions
Wenxin Xu, Jie Chen, Gerald Corzo, et al.
Water Resources Research (2024) Vol. 60, Iss. 2
Open Access | Times Cited: 16
Explainable artificial intelligence models for mineral prospectivity mapping
Renguang Zuo, Qiuming Cheng, Ying Xu, et al.
Science China Earth Sciences (2024) Vol. 67, Iss. 9, pp. 2864-2875
Closed Access | Times Cited: 16
Geological Knowledge‐Guided Dual‐Branch Deep Learning Model for Identification of Geochemical Anomalies Related to Mineralization
Ying Xu, Renguang Zuo, Yang Bai
Journal of Geophysical Research Machine Learning and Computation (2025) Vol. 2, Iss. 1
Open Access | Times Cited: 1
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
Farshid Rahmani, Kathryn Lawson, Wenyu Ouyang, et al.
Environmental Research Letters (2020)
Open Access | Times Cited: 117
Simulating runoff under changing climatic conditions: A comparison of the long short-term memory network with two conceptual hydrologic models
Peng Bai, Xiaomang Liu, Jiaxin Xie
Journal of Hydrology (2020) Vol. 592, pp. 125779-125779
Closed Access | Times Cited: 99
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