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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:

Forecasting Multiple Groundwater Time Series with Local and Global Deep Learning Networks
Stephanie Clark, Dan Pagendam, Louise Ryan
International Journal of Environmental Research and Public Health (2022) Vol. 19, Iss. 9, pp. 5091-5091
Open Access | Times Cited: 20

Showing 20 citing articles:

Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis
Stephen Afrifa, Tao Zhang, Peter Appiahene, et al.
Future Internet (2022) Vol. 14, Iss. 9, pp. 259-259
Open Access | Times Cited: 49
On the challenges of global entity-aware deep learning models for groundwater level prediction
Benedikt Heudorfer, Tanja Liesch, Stefan Broda
Hydrology and earth system sciences (2024) Vol. 28, Iss. 3, pp. 525-543
Open Access | Times Cited: 12
Research on a Prediction Model of Water Quality Parameters in a Marine Ranch Based on LSTM-BP
He Xu, Bin Lv, Jie Chen, et al.
Water (2023) Vol. 15, Iss. 15, pp. 2760-2760
Open Access | Times Cited: 15
Correlation–based Reliability Index Equipped with Machine Learning Methods to Complete the Groundwater Level Gaps
Seyed Hossein Hosseini, Ramtin Moeini
Results in Engineering (2025), pp. 104146-104146
Open Access
Global or local modeling for XGBoost in geospatial studies upon simulated data and German COVID-19 infection forecasting
Ximeng Cheng, Jackie Ma
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Dynamic Real-Time Prediction of Reclaimed Water Volumes Using the Improved Transformer Model and Decomposition Integration Technology
Xiangyu Sun, Lina Zhang, Chao Wang, et al.
Sustainability (2024) Vol. 16, Iss. 15, pp. 6598-6598
Open Access | Times Cited: 4
An Overview of Deep Learning Applications in Groundwater Level Modeling: Bridging the Gap between Academic Research and Industry Applications
Ahmed Ali, Farhad Jazaei, Peyman Babakhani, et al.
Applied Computational Intelligence and Soft Computing (2024) Vol. 2024, Iss. 1
Open Access | Times Cited: 2
Multi-Task Time Series Forecasting Based on Graph Neural Networks
Han Xiao, Yongjie Huang, Zhisong Pan, et al.
Entropy (2023) Vol. 25, Iss. 8, pp. 1136-1136
Open Access | Times Cited: 6
A log-additive neural model for spatio-temporal prediction of groundwater levels
Dan Pagendam, Sreekanth Janardhanan, Joel Janek Dabrowski, et al.
Spatial Statistics (2023) Vol. 55, pp. 100740-100740
Closed Access | Times Cited: 5
Algorithmic Management in Scientific Research
Maximilian Koehler, Henry Sauermann
SSRN Electronic Journal (2023)
Closed Access | Times Cited: 2
Un enfoque de regresión armónica dinámica para estimar la evapotranspiración de aguas subterráneas basado en las fluctuaciones diarias del nivel freático
Rebecca Doble, Glen Walker, Russell S. Crosbie, et al.
Hydrogeology Journal (2023) Vol. 32, Iss. 1, pp. 59-80
Open Access | Times Cited: 2
Testing the Possibilities and Limits of Groundwater Level Prediction Using Global Model Frameworks with Deep Learning on a Worldwide Scale
Annika Nolte, Benedikt Heudorfer, Steffen Bender, et al.
(2024)
Closed Access
Urban Water Demand Forecasting Using DeepAR-Models as Part of the Battle of Water Demand Forecasting (BWDF)
Andreas Wünsch, Christian Kühnert, Steffen Wallner, et al.
(2024) Vol. 9, pp. 25-25
Open Access
Forecasting Underground Water Levels: LSTM Based Model Outperforms GRU and Decision Tree Based Models
Md. Jafril Alam, Sujoy Kumar Kar, Sakib Zaman, et al.
2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE) (2022), pp. 280-283
Closed Access | Times Cited: 1
On the challenges of global entity-aware deep learning models for groundwater level prediction
Benedikt Heudorfer, Tanja Liesch, Stefan Broda
(2023)
Open Access
Comment on hess-2023-192
Benedikt Heudorfer, Tanja Liesch, Stefan Broda
(2023)
Open Access
Comment on hess-2023-192
Sayantan Majumdar
(2023)
Open Access
Reply on RC1
Benedikt Heudorfer
(2023)
Open Access
Reply on RC2
Benedikt Heudorfer
(2023)
Open Access
Spatial and Temporal Patterns of Groundwater Levels: A Case Study of Alluvial Aquifers in the Murray–Darling Basin, Australia
Guobin Fu, Stephanie Clark, Dennis Gonzalez, et al.
Sustainability (2023) Vol. 15, Iss. 23, pp. 16295-16295
Open Access
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