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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:

Ensemble and stochastic conceptual data-driven approaches for improving streamflow simulations: Exploring different hydrological and data-driven models and a diagnostic tool
David Hah, John Quilty, Anna E. Sikorska‐Senoner
Environmental Modelling & Software (2022) Vol. 157, pp. 105474-105474
Open Access | Times Cited: 12

Showing 12 citing articles:

A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting
Mohammad Sina Jahangir, John You, John Quilty
Journal of Hydrology (2023) Vol. 619, pp. 129269-129269
Closed Access | Times Cited: 36
A hybrid model coupling process-driven and data-driven models for improved real-time flood forecasting
Chengjing Xu, Ping‐an Zhong, Feilin Zhu, et al.
Journal of Hydrology (2024) Vol. 638, pp. 131494-131494
Closed Access | Times Cited: 5
Assessing the simulation of streamflow with the LSTM model across the continental United States using the MOPEX dataset
Achraf Tounsi, Mohamed Abdelkader, Marouane Temimi
Neural Computing and Applications (2023) Vol. 35, Iss. 30, pp. 22469-22486
Closed Access | Times Cited: 10
A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
Katarzyna Samborska-Goik, Marta Pogrzeba
Applied Sciences (2024) Vol. 14, Iss. 9, pp. 3675-3675
Open Access | Times Cited: 3
Enhancing the streamflow simulation of a process-based hydrological model using machine learning and multi-source data
Huajin Lei, Hongyi Li, Wanpin Hu
Ecological Informatics (2024) Vol. 82, pp. 102755-102755
Open Access | Times Cited: 3
A Hybrid, Non‐Stationary Stochastic Watershed Model (SWM) for Uncertain Hydrologic Simulations Under Climate Change
Zachary Paul Brodeur, Sungwook Wi, Ghazal Shabestanipour, et al.
Water Resources Research (2024) Vol. 60, Iss. 5
Open Access | Times Cited: 2
A stochastic deep-learning-based approach for improved streamflow simulation
Neda Dolatabadi, Banafsheh Zahraie
Stochastic Environmental Research and Risk Assessment (2023) Vol. 38, Iss. 1, pp. 107-126
Closed Access | Times Cited: 4
Quantifying Streamflow Prediction Uncertainty Through Process‐Aware Data‐Driven Models
Abhinanda Roy, K. S. Kasiviswanathan
Hydrological Processes (2024) Vol. 38, Iss. 11
Closed Access
A novel physical process-ensemble learning model framework with residual error decomposition to upskill daily runoff prediction
Yan Kang, Y. J. Xiang, Z. N. Zhang, et al.
Journal of Hydrology (2024), pp. 132565-132565
Closed Access
A hybrid, non-stationary Stochastic Watershed Model (SWM) for uncertain hydrologic projections under climate change
Zachary Paul Brodeur, Sungwook Wi, Ghazal Shabestanipour, et al.
Authorea (Authorea) (2023)
Open Access
A multisite Stochastic Watershed Model (SWM) with intermittency for regional low flow and flood risk analysis
Zachary Paul Brodeur, Rohini S. Gupta, Scott Steinschneider
Authorea (Authorea) (2023)
Open Access
Modelagem ARX como ferramenta complementar aos estudos de estacionariedade da vazão em bacias hidrográficas
Rafael Iria Rodrigues
(2023)
Open Access
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