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:
Metamorphic testing of machine learning and conceptual hydrologic models
Peter Reichert, Kai Ma, Marvin Höge, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 11, pp. 2505-2529
Open Access | Times Cited: 4
Peter Reichert, Kai Ma, Marvin Höge, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 11, pp. 2505-2529
Open Access | Times Cited: 4
Showing 4 citing articles:
Are Deep Learning Models in Hydrology Entity Aware?
Benedikt Heudorfer, Hoshin V. Gupta, Ralf Loritz
Geophysical Research Letters (2025) Vol. 52, Iss. 6
Open Access
Benedikt Heudorfer, Hoshin V. Gupta, Ralf Loritz
Geophysical Research Letters (2025) Vol. 52, Iss. 6
Open Access
Detection and attribution of eco-hydrological alteration based on deep learning-driven gap-filled runoff in a large-scale catchment
Zhibao Dong, Xuan Ji, Kai Ma
Journal of Hydrology Regional Studies (2025) Vol. 58, pp. 102228-102228
Closed Access
Zhibao Dong, Xuan Ji, Kai Ma
Journal of Hydrology Regional Studies (2025) Vol. 58, pp. 102228-102228
Closed Access
On the value of a history of hydrology and the establishment of a History of Hydrology Working Group
Keith Beven, S. A. Archfield, Okke Batelaan, et al.
Hydrological Sciences Journal (2025), pp. 1-13
Open Access
Keith Beven, S. A. Archfield, Okke Batelaan, et al.
Hydrological Sciences Journal (2025), pp. 1-13
Open 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: 3
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: 3
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
Leilei He, Liangsheng Shi, Wenxiang Song, et al.
Water Resources Research (2024) Vol. 60, Iss. 12
Open Access | Times Cited: 1
