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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:

Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States
Hejiang Cai, Haiyun Shi, Suning Liu, et al.
Journal of Hydrology Regional Studies (2021) Vol. 37, pp. 100930-100930
Open Access | Times Cited: 63

Showing 1-25 of 63 citing articles:

Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for hydrological processes
Pravin Bhasme, Jenil Vagadiya, Udit Bhatia
Journal of Hydrology (2022) Vol. 615, pp. 128618-128618
Open Access | Times Cited: 83
Applications of machine learning to water resources management: A review of present status and future opportunities
Ashraf Ahmed, Sakina Sayed, Antoifi Abdoulhalik, et al.
Journal of Cleaner Production (2024) Vol. 441, pp. 140715-140715
Open Access | Times Cited: 63
Groundwater level forecasting with machine learning models: A review
Kenneth Beng Wee Boo, Ahmed El‐Shafie, Faridah Othman, et al.
Water Research (2024) Vol. 252, pp. 121249-121249
Closed Access | Times Cited: 31
Enhancing the accuracy of groundwater level prediction at different scales using spatio-temporal graph convolutional model
Long Chen, Dezheng Zhang, Jianwei Xu, et al.
Earth Science Informatics (2025) Vol. 18, Iss. 2
Closed Access | Times Cited: 2
Graph neural network for groundwater level forecasting
Tao Bai, Pejman Tahmasebi
Journal of Hydrology (2022) Vol. 616, pp. 128792-128792
Closed Access | Times Cited: 60
A New Perspective on Drought Propagation: Causality
Haiyun Shi, Yiyang Zhao, Suning Liu, et al.
Geophysical Research Letters (2022) Vol. 49, Iss. 2
Closed Access | Times Cited: 48
Using machine learning algorithms to predict groundwater levels in Indonesian tropical peatlands
Iman Salehi Hikouei, Keith N. Eshleman, Bambang Hero Saharjo, et al.
The Science of The Total Environment (2022) Vol. 857, pp. 159701-159701
Open Access | Times Cited: 43
Novel Ensemble Machine Learning Modeling Approach for Groundwater Potential Mapping in Parbhani District of Maharashtra, India
Md Masroor, Haroon Sajjad, Pankaj Kumar, et al.
Water (2023) Vol. 15, Iss. 3, pp. 419-419
Open Access | Times Cited: 29
Application of machine learning and emerging remote sensing techniques in hydrology: A state-of-the-art review and current research trends
Asish Saha, Subodh Chandra Pal
Journal of Hydrology (2024) Vol. 632, pp. 130907-130907
Closed Access | Times Cited: 16
Towards efficient irrigation management at field scale using new technologies: A systematic literature review
Afaf Bounajra, Kamal El Guemmat, Khalifa Mansouri, et al.
Agricultural Water Management (2024) Vol. 295, pp. 108758-108758
Open Access | Times Cited: 11
Explaining the Mechanism of Multiscale Groundwater Drought Events: A New Perspective From Interpretable Deep Learning Model
Hejiang Cai, Haiyun Shi, Zhaoqiang Zhou, et al.
Water Resources Research (2024) Vol. 60, Iss. 7
Open Access | Times Cited: 10
Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets
F. M. Hasan, Paul Medley, Jason Drake, et al.
Water (2024) Vol. 16, Iss. 13, pp. 1904-1904
Open Access | Times Cited: 9
A wavelet-assisted deep learning approach for simulating groundwater levels affected by low-frequency variability
Sivarama Krishna Reddy Chidepudi, Nicolas Masséi, Abderrahim Jardani, et al.
The Science of The Total Environment (2022) Vol. 865, pp. 161035-161035
Open Access | Times Cited: 34
Can transfer learning improve hydrological predictions in the alpine regions?
Yingying Yao, Yufeng Zhao, Xin Li, et al.
Journal of Hydrology (2023) Vol. 625, pp. 130038-130038
Closed Access | Times Cited: 21
Harnessing Machine Learning for Assessing Climate Change Influences on Groundwater Resources: A Comprehensive Review
Apoorva Bamal, Md Galal Uddin, Agnieszka I. Olbert
Heliyon (2024) Vol. 10, Iss. 17, pp. e37073-e37073
Open Access | Times Cited: 8
Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins
Haoyuan Yu, Qichun Yang
Water (2024) Vol. 16, Iss. 15, pp. 2199-2199
Open Access | Times Cited: 7
Application of Artificial Intelligence Techniques for the Determination of Groundwater Level Using Spatio–Temporal Parameters
Amirhossein Najafabadipour, Gholamreza Kamali, Hossein Nezamabadi‐pour
ACS Omega (2022) Vol. 7, Iss. 12, pp. 10751-10764
Open Access | Times Cited: 23
An interpretable machine learning model for predicting cavity water depth and cavity length based on XGBoost–SHAP
Tiexiang Mo, Shanshan Li, Guodong Li
Journal of Hydroinformatics (2023) Vol. 25, Iss. 4, pp. 1488-1500
Open Access | Times Cited: 14
Determination of groundwater buoyancy reduction coefficient in clay: Model tests, numerical simulations and machine learning methods
Weixin Sun, H. L. Liu, Wengang Zhang, et al.
Underground Space (2023) Vol. 13, pp. 228-240
Open Access | Times Cited: 14
Realising smarter stormwater management: A review of the barriers and a roadmap for real world application
Chris Sweetapple, James L. Webber, Anna Hastings, et al.
Water Research (2023) Vol. 244, pp. 120505-120505
Open Access | Times Cited: 14
Mapping groundwater potentiality by using hybrid machine learning models under the scenario of climate variability: a national level study of Bangladesh
Showmitra Kumar Sarkar, Fahad Alshehri, Shahfahad, et al.
Environment Development and Sustainability (2024)
Closed Access | Times Cited: 6
Groundwater dynamics clustering and prediction based on grey relational analysis and LSTM model: A case study in Beijing Plain, China
Yan Zhou, Qiulan Zhang, Guoying Bai, et al.
Journal of Hydrology Regional Studies (2024) Vol. 56, pp. 102011-102011
Open Access | Times Cited: 6
Comparison of Classical and Machine Learning Methods in Estimation of Missing Streamflow Data
Alireza B. Dariane, Matineh Imani Borhan
Water Resources Management (2024) Vol. 38, Iss. 4, pp. 1453-1478
Closed Access | Times Cited: 5
Improving the interpretability and predictive power of hydrological models: Applications for daily streamflow in managed and unmanaged catchments
Pravin Bhasme, Udit Bhatia
Journal of Hydrology (2023) Vol. 628, pp. 130421-130421
Closed Access | Times Cited: 12
Predictive Modeling of Groundwater Recharge under Climate Change Scenarios in the Northern Area of Saudi Arabia
Rabie Α. Ramadan, Sahbi Boubaker
Engineering Technology & Applied Science Research (2024) Vol. 14, Iss. 2, pp. 13578-13583
Open Access | Times Cited: 4
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