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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:

HESS Opinions: Never train an LSTM on a single basin
Frederik Kratzert, Martin Gauch, Daniel Klotz, et al.
(2024)
Open Access | Times Cited: 35

Showing 1-25 of 35 citing articles:

Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence
Sinan Rasiya Koya, Tirthankar Roy
Journal of Hydrology (2024) Vol. 637, pp. 131301-131301
Open Access | Times Cited: 19
Probing the limit of hydrologic predictability with the Transformer network
Jiangtao Liu, Yuchen Bian, Kathryn Lawson, et al.
Journal of Hydrology (2024) Vol. 637, pp. 131389-131389
Open Access | Times Cited: 12
To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization
Eduardo Acuña Espinoza, Ralf Loritz, Manuel Álvarez Chaves, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 12, pp. 2705-2719
Open Access | Times Cited: 10
Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach
Qiutong Yu, Bryan A. Tolson, Hongren Shen, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 9, pp. 2107-2122
Open Access | Times Cited: 7
Spatial Sensitivity of River Flooding to Changes in Climate and Land Cover Through Explainable AI
Louise Slater, Gemma Coxon, Manuela I. Brunner, et al.
Earth s Future (2024) Vol. 12, Iss. 5
Open Access | Times Cited: 6
Large-sample hydrology – a few camels or a whole caravan?
Franziska Clerc-Schwarzenbach, Giovanni Selleri, Mattia Neri, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 17, pp. 4219-4237
Open Access | Times Cited: 5
Reply on RC2
Stefan Kunz
(2025)
Closed Access
Reply on RC3
Stefan Kunz
(2025)
Closed Access
Establishing performance criteria for evaluating watershed-scale sediment and nutrient models at fine temporal scales
Aniruddha B. Pandit, Sarah B. Hogan, David Tyler Mahoney, et al.
Water Research (2025) Vol. 274, pp. 123156-123156
Open Access
Improved streamflow simulations in hydrologically diverse basins using physically informed deep learning models
Bhanu Magotra, Manabendra Saharia, C. T. Dhanya
Hydrological Sciences Journal (2025)
Closed Access
Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application
Xueying Chen, Yuhang Zhang, Aizhong Ye, et al.
Environmental Modelling & Software (2025), pp. 106350-106350
Open Access
Enhancing representation of data-scarce reservoir-regulated river basins using a hybrid DL-process based approach
Liangkun Deng, Xiang Zhang, Louise Slater
Journal of Hydrology (2025) Vol. 655, pp. 132895-132895
Closed Access
Dive into transfer-learning for daily rainfall-runoff modeling in data-limited basins
Wenyu Ouyang, Chi Zhang, Lei Ye, et al.
Journal of Hydrology (2025), pp. 133063-133063
Closed Access
Enhancing estuary salinity prediction: A Machine Learning and Deep Learning based approach
Leonardo Saccotelli, Giorgia Verri, Alessandro De Lorenzis, et al.
Applied Computing and Geosciences (2024) Vol. 23, pp. 100173-100173
Open Access | Times Cited: 4
Streamflow Prediction at the Intersection of Physics and Machine Learning: A Case Study of Two Mediterranean‐Climate Watersheds
Saalem Adera, Dino Bellugi, A. S. Dhakal, et al.
Water Resources Research (2024) Vol. 60, Iss. 7
Open Access | Times Cited: 4
Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures
Abhinav Gupta, Mohamed M. Hantush, Rao S. Govindaraju, et al.
Journal of Hydrology (2024) Vol. 641, pp. 131774-131774
Open Access | Times Cited: 4
Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources
Jared Willard, Charuleka Varadharajan, Xiaowei Jia, et al.
Environmental Data Science (2025) Vol. 4
Open Access
Predicting the Evolution of Extreme Water Levels With Long Short‐Term Memory Station‐Based Approximated Models and Transfer Learning Techniques
Samuel Daramola, David F. Muñoz, Paúl Muñoz, et al.
Water Resources Research (2025) Vol. 61, Iss. 3
Open Access
Investigating the streamflow simulation capability of a new mass-conserving long short-term memory (MC-LSTM) model across the contiguous United States
Y Wang, Lujun Zhang, N. Benjamin Erichson, et al.
Journal of Hydrology (2025), pp. 133161-133161
Closed Access
Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain
Fateme Hosseini, Cristina Prieto, César Álvarez
Journal of Hydrology (2024), pp. 132003-132003
Open Access | Times Cited: 3
A short history of philosophies of hydrological model evaluation and hypothesis testing
Keith Beven
Wiley Interdisciplinary Reviews Water (2024)
Open Access | Times Cited: 2
Investigating the Model Hypothesis Space: Benchmarking Automatic Model Structure Identification With a Large Model Ensemble
Diana Spieler, Niels Schütze
Water Resources Research (2024) Vol. 60, Iss. 7
Open Access | Times Cited: 1
Assessing the adequacy of traditional hydrological models for climate change impact studies: A case for long-short-term memory (LSTM) neural networks
Jean‐Luc Martel, François Brissette, Richard Arsenault, et al.
(2024)
Open Access | Times Cited: 1
Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data
Kshitij Tayal, Arvind Renganathan, Dan Lu
Environmental Research Letters (2024) Vol. 19, Iss. 10, pp. 104009-104009
Open Access | Times Cited: 1
A data-centric perspective on the information needed for hydrological uncertainty predictions
Andreas Auer, Martin Gauch, Frederik Kratzert, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 17, pp. 4099-4126
Open Access | Times Cited: 1
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