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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:

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: 11

Showing 11 citing articles:

Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what is the best way to leverage regionalised information?
Sivarama Krishna Reddy Chidepudi, Nicolas Masseï, Abderrahim Jardani, et al.
Hydrology and earth system sciences (2025) Vol. 29, Iss. 4, pp. 841-861
Open Access
Direct impact of climate change on groundwater levels in the Iberian Peninsula
Amir Rouhani, Nahed Ben-Salem, Marco D’Oria, et al.
The Science of The Total Environment (2025) Vol. 970, pp. 179009-179009
Open Access
Impact of Climate Change on Groundwater Level Changes: An Evaluation Based on Deep Neural Networks
Stephen Afrifa, Tao Zhang, Peter Appiahene, et al.
Applied Computational Intelligence and Soft Computing (2025) Vol. 2025, Iss. 1
Open Access
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: 4
Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what’s the best way to leverage regionalised information?
Sivarama Krishna Reddy Chidepudi, Nicolas Masséi, Abderrahim Jardani, et al.
(2024)
Open Access | Times Cited: 1
Comment on egusphere-2024-794
Sivarama Krishna Reddy Chidepudi, Nicolas Masséi, Abderrahim Jardani, et al.
(2024)
Open Access
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
Reply on RC1
Sivarama Krishna Reddy Chidepudi
(2024)
Open Access
Spatial and temporal forecasting of groundwater anomalies in complex aquifer undergoing climate and land use change
Ammara Talib, Ankur R. Desai, Jingyi Huang
Journal of Hydrology (2024) Vol. 639, pp. 131525-131525
Open Access
Reply on RC2
Sivarama Krishna Reddy Chidepudi
(2024)
Open Access
Groundwater level projections for aquifers affected by annual to decadal hydroclimate variations
Sivarama Krishna Reddy Chidepudi, Nicolas Masséi, Abderrahim Jardani, et al.
Authorea (Authorea) (2024)
Open Access
Assessing groundwater level modelling using a 1-D convolutional neural network (CNN): linking model performances to geospatial and time series features
Mariana Gomez, Maximilian Nölscher, Andreas Hartmann, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 19, pp. 4407-4425
Open Access
Associations between deep learning runoff predictions and hydrogeological conditions in Australia
Stephanie Clark, Jasmine B.D. Jaffrés
Journal of Hydrology (2024), pp. 132569-132569
Closed Access
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