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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:

Estimation of base and surface flow using deep neural networks and a hydrologic model in two watersheds of the Chesapeake Bay
Jiye Lee, Ather Abbas, Gregory W. McCarty, et al.
Journal of Hydrology (2022) Vol. 617, pp. 128916-128916
Open Access | Times Cited: 17

Showing 17 citing articles:

Assessment of the impact of climate change on streamflow of Ganjiang River catchment via LSTM-based models
Chao Deng, Xin Yin, Jiacheng Zou, et al.
Journal of Hydrology Regional Studies (2024) Vol. 52, pp. 101716-101716
Open Access | Times Cited: 13
Enhancing flow rate prediction of the Chao Phraya River Basin using SWAT–LSTM model coupling
Kritnipit Phetanan, Seok Min Hong, Daeun Yun, et al.
Journal of Hydrology Regional Studies (2024) Vol. 53, pp. 101820-101820
Open Access | Times Cited: 11
Estimation of soil moisture in drip-irrigated citrus orchards using multi-modal UAV remote sensing
Zongjun Wu, Ningbo Cui, Wenjiang Zhang, et al.
Agricultural Water Management (2024) Vol. 302, pp. 108972-108972
Open Access | Times Cited: 10
A hybrid deep learning approach for streamflow prediction utilizing watershed memory and process-based modeling
Bisrat Ayalew Yifru, Kyoung Jae Lim, Joo Hyun Bae, et al.
Hydrology Research (2024) Vol. 55, Iss. 4, pp. 498-518
Open Access | Times Cited: 9
Nature-based solutions as buffers against coastal compound flooding: Exploring potential framework for process-based modeling of hazard mitigation
Soheil Radfar, Sadaf Mahmoudi, Hamed Moftakhari, et al.
The Science of The Total Environment (2024) Vol. 938, pp. 173529-173529
Closed Access | Times Cited: 4
A deep learning approach for SMAP soil moisture downscaling informed by thermal inertia theory
Mengyuan Xu, Haoxuan Yang, Annan Hu, et al.
International Journal of Applied Earth Observation and Geoinformation (2025) Vol. 136, pp. 104370-104370
Closed Access
Distribution coefficient prediction using multimodal machine learning based on soil adsorption factors, XRF, and XRD spectrum data
Seongyeon Na, Heewon Jeong, Ilgook Kim, et al.
Journal of Hazardous Materials (2024) Vol. 478, pp. 135285-135285
Closed Access | Times Cited: 3
Interpretable machine learning guided by physical mechanisms reveals drivers of runoff under dynamic land use changes
Shu‐Li Wang, Yitian Liu, Wei Wang, et al.
Journal of Environmental Management (2024) Vol. 367, pp. 121978-121978
Closed Access | Times Cited: 3
Enhancing short-term streamflow prediction in the Haihe River Basin through integrated machine learning with Lasso
Yongyu Song, Jing Zhang
Water Science & Technology (2024) Vol. 89, Iss. 9, pp. 2367-2383
Open Access | Times Cited: 2
A novel deep learning rainfall–runoff model based on Transformer combined with base flow separation
Shu‐Li Wang, Wei Wang, Guizhang Zhao
Hydrology Research (2024) Vol. 55, Iss. 5, pp. 576-594
Open Access | Times Cited: 2
Autonomous real-time control for membrane capacitive deionization
Jaegyu Shim, Suin Lee, Nakyeong Yun, et al.
Water Research (2024) Vol. 262, pp. 122086-122086
Closed Access | Times Cited: 2
Nutritional status of the reservoir tributary backwater area and implications for nutrient control
Shibo Chen, Lei Chen, Xiaosha Zhi, et al.
Journal of Hydrology (2024) Vol. 643, pp. 131926-131926
Closed Access | Times Cited: 2
Comparison of Multilayer Perceptron with an Optimal Activation Function and Long Short-Term Memory for Rainfall-Runoff Simulations and Ungauged Catchment Runoff Prediction
Mun-Ju Shin, Yong Jung
Water Resources Management (2024)
Closed Access | Times Cited: 1
Bridging the Gap: An Interpretable Coupled Model (Swat-Elm-Shap) for Blue-Green Water Simulation in Data-Scarce Basins
Zhonghui Guo, Chang Feng, Yang Liu, et al.
(2024)
Closed Access
Bridging the gap: An interpretable coupled model (SWAT-ELM-SHAP) for blue-green water simulation in data-scarce basins
Zhonghui Guo, Chang Feng, Yang Liu, et al.
Agricultural Water Management (2024) Vol. 306, pp. 109157-109157
Open Access
Virtual special issue “Changes in hydrological processes and water resources in the context of climate change and carbon neutrality”
Huimin Lei, Xiaojun Wang, Yanlan Liu
Journal of Hydrology (2023) Vol. 627, pp. 130268-130268
Closed Access
Enhancing Short-Term Streamflow Prediction in the Haihe River Basin with Integrated Deep Learning with Lasso
Yongyu Song, Jing Zhang
(2023)
Closed Access
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