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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:

Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework
Tom Botterill, Hilary McMillan
Water Resources Research (2023) Vol. 59, Iss. 3
Open Access | Times Cited: 21

Showing 21 citing articles:

Long-term streamflow forecasting in data-scarce regions: Insightful investigation for leveraging satellite-derived data, Informer architecture, and concurrent fine-tuning transfer learning
Fatemeh Ghobadi, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Doosun Kang
Journal of Hydrology (2024) Vol. 631, pp. 130772-130772
Closed Access | Times Cited: 12
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
Enhancing streamflow simulation in large and human-regulated basins: Long short-term memory with multiscale attributes
Arken Tursun, Xianhong Xie, Yibing Wang, et al.
Journal of Hydrology (2024) Vol. 630, pp. 130771-130771
Closed Access | Times Cited: 10
Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model
Arken Tursun, Xianhong Xie, Yibing Wang, et al.
Journal of Hydrology Regional Studies (2024) Vol. 52, pp. 101744-101744
Open Access | Times Cited: 10
Multi-step ahead forecasting of daily streamflow based on the transform-based deep learning model under different scenarios
Miao He, Xian Xu, Shaofei Wu, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Understanding the inter-event variability of recession flow characteristics and its drivers
Owees Rashid, Tushar Apurv
Journal of Hydrology (2025), pp. 133033-133033
Closed Access
Development of a Distributed Physics‐Informed Deep Learning Hydrological Model for Data‐Scarce Regions
L. Zhong, Huimin Lei, Jingjing Yang
Water Resources Research (2024) Vol. 60, Iss. 6
Open Access | Times Cited: 4
Permeability prediction using logging data from tight reservoirs based on deep neural networks
Zhijian Fang, Jing Ba, José M. Carcione, et al.
Journal of Applied Geophysics (2024) Vol. 229, pp. 105501-105501
Closed Access | Times Cited: 4
When good signatures go bad: Applying hydrologic signatures in large sample studies
Hilary McMillan, Gemma Coxon, Ryoko Araki, et al.
Hydrological Processes (2023) Vol. 37, Iss. 9
Open Access | Times Cited: 11
Learning Landscape Features from Streamflow with Autoencoders
A. Bassi, Marvin Höge, Antonietta Mira, et al.
(2024)
Open Access | Times Cited: 2
Shear-wave velocity prediction of tight reservoirs based on poroelasticity theory: A comparative study of deep neural network and rock physics model
Zhijian Fang, Jing Ba, Qiang Guo, et al.
Geoenergy Science and Engineering (2024) Vol. 240, pp. 213028-213028
Closed Access | Times Cited: 1
Streamflow Prediction in Human‐Regulated Catchments Using Multiscale Deep Learning Modeling With Anthropogenic Similarities
Arken Tursun, Xianhong Xie, Yibing Wang, et al.
Water Resources Research (2024) Vol. 60, Iss. 9
Open Access | Times Cited: 1
Rapid prediction of urban flooding at street-scale using physics-informed machine learning-based surrogate modeling
Yogesh Bhattarai, Sunil Bista, Rocky Talchabhadel, et al.
Total Environment Advances (2024) Vol. 12, pp. 200116-200116
Closed Access | Times Cited: 1
Learning landscape features from streamflow with autoencoders
A. Bassi, Marvin Höge, Antonietta Mira, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 22, pp. 4971-4988
Open Access | Times Cited: 1
Comment on hess-2024-47

(2024)
Open Access
Comment on hess-2024-47

(2024)
Open Access
Comment on hess-2024-47

(2024)
Open Access
Reply on RC3
A. Bassi
(2024)
Open Access
Reply on RC3
A. Bassi
(2024)
Open Access
Developing a data-driven water quality prediction tool at the event scale for tile-drained agriculture-dominated catchments
Carlos Ignacio Calderon Salinas de Lozada
(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
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