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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:

Review: Theory-guided machine learning applied to hydrogeology—state of the art, opportunities and future challenges
Adoubi Vincent De Paul Adombi, Romain Chesnaux, Marie‐Amélie Boucher
Hydrogeology Journal (2021) Vol. 29, Iss. 8, pp. 2671-2683
Open Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting
Mohammad Sina Jahangir, John You, John Quilty
Journal of Hydrology (2023) Vol. 619, pp. 129269-129269
Closed Access | Times Cited: 41
A New Benchmark on Machine Learning Methodologies for Hydrological Processes Modelling: A Comprehensive Review for Limitations and Future Research Directions
Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬
Knowledge-Based Engineering and Sciences (2023) Vol. 4, Iss. 3, pp. 65-103
Open Access | Times Cited: 25
Prediction of sulfate concentrations in groundwater in areas with complex hydrogeological conditions based on machine learning
Yushan Tian, Quanli Liu, Yao Ji, et al.
The Science of The Total Environment (2024) Vol. 923, pp. 171312-171312
Closed Access | Times Cited: 10
Advancing groundwater quality predictions: Machine learning challenges and solutions
Juan Antonio Torres-Martínez, Jürgen Mahlknecht, Manish Kumar, et al.
The Science of The Total Environment (2024) Vol. 949, pp. 174973-174973
Closed Access | Times Cited: 10
A stochastic conceptual-data-driven approach for improved hydrological simulations
John Quilty, Anna E. Sikorska‐Senoner, David Hah
Environmental Modelling & Software (2022) Vol. 149, pp. 105326-105326
Open Access | Times Cited: 37
Solving groundwater flow equation using physics-informed neural networks
Salvatore Cuomo, Mariapia De Rosa, Fabio Giampaolo, et al.
Computers & Mathematics with Applications (2023) Vol. 145, pp. 106-123
Closed Access | Times Cited: 16
Monthly Streamflow Prediction by Metaheuristic Regression Approaches Considering Satellite Precipitation Data
Mojtaba Mehraein, Aadhityaa Mohanavelu, Sujay Raghavendra Naganna, et al.
Water (2022) Vol. 14, Iss. 22, pp. 3636-3636
Open Access | Times Cited: 21
A causal physics-informed deep learning formulation for groundwater flow modeling and climate change effect analysis
Adoubi Vincent De Paul Adombi, Romain Chesnaux, Marie‐Amélie Boucher, et al.
Journal of Hydrology (2024) Vol. 637, pp. 131370-131370
Open Access | Times Cited: 4
A study of mechanism-data hybrid-driven method for multibody system via physics-informed neural network
Ningning Song, Chuanda Wang, Haijun Peng, et al.
Acta Mechanica Sinica (2024) Vol. 41, Iss. 3
Closed Access | Times Cited: 4
Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices
Caixia Hu, Jie Li, Yong Pang, et al.
Land (2025) Vol. 14, Iss. 1, pp. 69-69
Open Access
Analyzing the effects of data splitting and covariate shift on machine learning based streamflow prediction in ungauged basins
William Crossley, Sayan Dey, Venkatesh Merwade
Journal of Hydrology (2025), pp. 132731-132731
Closed Access
Machine Learning Techniques in Hydrogeological Research
Song He, Xiaoping Zhou, Yuan Liu, et al.
Springer hydrogeology (2025), pp. 137-164
Closed Access
Predicting dissolved oxygen in water areas using transfer learning and visual information from real-time surveillance videos
Jihong Wang, Yituo Zhang, Chaolin Li, et al.
Journal of Cleaner Production (2025), pp. 145547-145547
Closed Access
A generalizable framework of solution-guided machine learning with application to nanoindentation of free-standing thin films
Ruijin Wang, Tianquan Ying, Chen Yang, et al.
Thin-Walled Structures (2024) Vol. 200, pp. 111984-111984
Closed Access | Times Cited: 3
Reconstrucción probabilística mediante aprendizaje automático del sistema acuífero de la cuenca del Po (Italia)
Andrea Manzoni, Giovanni Porta, Laura Guadagnini, et al.
Hydrogeology Journal (2023) Vol. 31, Iss. 6, pp. 1547-1563
Open Access | Times Cited: 8
Ensemble and stochastic conceptual data-driven approaches for improving streamflow simulations: Exploring different hydrological and data-driven models and a diagnostic tool
David Hah, John Quilty, Anna E. Sikorska‐Senoner
Environmental Modelling & Software (2022) Vol. 157, pp. 105474-105474
Open Access | Times Cited: 12
Improved monthly streamflow prediction using integrated multivariate adaptive regression spline with K-means clustering: implementation of reanalyzed remote sensing data
Özgür Kişi, Salim Heddam, Kulwinder Singh Parmar, et al.
Stochastic Environmental Research and Risk Assessment (2024) Vol. 38, Iss. 6, pp. 2489-2519
Open Access | Times Cited: 2
Contribution to advancing aquifer geometric mapping using machine learning and deep learning techniques: a case study of the AL Haouz-Mejjate aquifer, Marrakech, Morocco
Lhoussaine El Mezouary, Abdessamad Hadri, Mohamed Hakim Kharrou, et al.
Applied Water Science (2024) Vol. 14, Iss. 5
Open Access | Times Cited: 2
Enhancing short-term streamflow prediction in the Haihe River Basin through integrated machine learning with Lasso
Yongyu Song, Jing Zhang
Water Science & Technology (2024) Vol. 89, Iss. 9, pp. 2367-2383
Open Access | Times Cited: 2
An automated machine learning methodology for the improved prediction of reference evapotranspiration using minimal input parameters
Sowmya Mangalath Ravindran, Santosh Kumar Moorakkal Bhaskaran, Sooraj K. Ambat, et al.
Hydrological Processes (2022) Vol. 36, Iss. 5
Closed Access | Times Cited: 10
Comparing numerical modelling, traditional machine learning and theory-guided machine learning in inverse modeling of groundwater dynamics: A first study case application
Adoubi Vincent De Paul Adombi, Romain Chesnaux, Marie‐Amélie Boucher
Journal of Hydrology (2022) Vol. 615, pp. 128600-128600
Closed Access | Times Cited: 9
Analyzing the Effects of Data Splitting and Covariate Shift on Machine Learning Based Streamflow Prediction in Ungauged Basins
William Crossley, Sayan Dey, Venkatesh Merwade
(2024)
Closed Access | Times Cited: 1
Avaliação da capacidade exploratória de aquíferos fraturados: um estudo de caso para os sistemas aquíferos da porção sul do Rio Grande do Sul, Brasil
Mauren E. Gaspar, Christie Helouise Engelmann de Oliveira, Francisco Manoel Wohnrath Tognoli
Águas Subterrâneas (2023) Vol. 37, Iss. 1
Open Access | Times Cited: 1
Empirical, Statistical, and Machine Learning Techniques for Predicting Surface Settlement Induced by Tunnelling
Chia Yu Huat, Danial Jahed Armaghani, Ehsan Momeni, et al.
(2023), pp. 39-77
Closed Access | Times Cited: 1
Investigating the role of ENSO in groundwater temporal variability across Abu Dhabi Emirate, United Arab Emirates using machine learning algorithms
Khaled Alghafli, Xiaogang Shi, William T. Sloan, et al.
Groundwater for Sustainable Development (2024) Vol. 28, pp. 101389-101389
Open Access
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