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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:

Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling
Farshid Rahmani, Alison P. Appling, Dapeng Feng, et al.
Water Resources Research (2023) Vol. 59, Iss. 12
Open Access | Times Cited: 9

Showing 9 citing articles:

Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning
Tadd Bindas, Wen‐Ping Tsai, Jiangtao Liu, et al.
Water Resources Research (2024) Vol. 60, Iss. 1
Open Access | Times Cited: 24
Landslide susceptibility mapping using physics-guided machine learning: a case study of a debris flow event in Colorado Front Range
Te Pei, Tong Qiu
Acta Geotechnica (2024) Vol. 19, Iss. 10, pp. 6617-6641
Closed Access | Times Cited: 6
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
Regional stream temperature modeling in pristine Atlantic salmon rivers: A hybrid deterministic–Machine Learning approach
Ilias Hani, André St‐Hilaire, Taha B. M. J. Ouarda
Journal of Hydrology Regional Studies (2025) Vol. 59, pp. 102373-102373
Closed Access
Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL)
Dapeng Feng, Hylke E. Beck, Jens de Bruijn, et al.
Geoscientific model development (2024) Vol. 17, Iss. 18, pp. 7181-7198
Open Access | Times Cited: 4
Multi-layer grid-scale soil moisture estimation using spatiotemporal deep learning methods with physical constraints
Tuantuan Zhang, Zhongmin Liang, Jianhong Zhou, et al.
Journal of Hydrology (2025), pp. 133086-133086
Closed Access
Improving process-based prediction of stream water temperature in SWAT using semi-Lagrangian formulation
Nigus Demelash Melaku, Christopher W. Brown, Ahmad A. Tavakoly
Journal of Hydrology (2024), pp. 132612-132612
Closed Access | Times Cited: 1
Machine Learning in Stream/River Water Temperature Modelling: a review and metrics for evaluation
Claudia Corona, T. S. Hogue
(2024)
Open Access | Times Cited: 1
Synergizing Intuitive Physics and Big Data in Deep Learning: Can We Obtain Process Insights While Maintaining State‐Of‐The‐Art Hydrological Prediction Capability?
Leilei He, Liangsheng Shi, Wenxiang Song, et al.
Water Resources Research (2024) Vol. 60, Iss. 12
Open Access | Times Cited: 1
Does grouping watersheds by hydrographic regions offer any advantages in fine-tuning transfer learning model for temporal and spatial streamflow predictions?
Yegane Khoshkalam, Alain N. Rousseau, Farshid Rahmani, et al.
Journal of Hydrology (2024), pp. 132540-132540
Closed Access
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