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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:

Development of a physics-informed data-driven model for gaining insights into hydrological processes in irrigated watersheds
Kailong Li, Guohe Huang, Shuo Wang, et al.
Journal of Hydrology (2022) Vol. 613, pp. 128323-128323
Closed Access | Times Cited: 17

Showing 17 citing articles:

Developing a Physics‐Informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments
L. Zhong, Huimin Lei, Bing Gao
Water Resources Research (2023) Vol. 59, Iss. 6
Closed Access | Times Cited: 35
Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review
Bisrat Ayalew Yifru, Kyoung Jae Lim, Seoro Lee
Sustainability (2024) Vol. 16, Iss. 4, pp. 1376-1376
Open Access | Times Cited: 9
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
A unified deep learning framework for water quality prediction based on time-frequency feature extraction and data feature enhancement
Rui Xu, Shengri Hu, Hang Wan, et al.
Journal of Environmental Management (2023) Vol. 351, pp. 119894-119894
Closed Access | Times Cited: 17
Convergent and Transdisciplinary Integration: On the Future of Integrated Modeling of Human‐Water Systems
Saman Razavi, Ashleigh Duffy, Leila Eamen, et al.
Water Resources Research (2025) Vol. 61, Iss. 2
Open Access
Enhancing daily runoff forecasting in hydropower basins with a voting ensemble model using historical data
Ngoc Anh Le, Phong Nguyen Thanh, Nhat Truong Pham, et al.
Hydrological Sciences Journal (2025), pp. 1-13
Closed Access
Bayesian ensemble learning and Shapley additive explanations for fast estimation of slope stability with a physics-informed database
Dongze Lei, Junwei Ma, Guangcheng Zhang, et al.
Natural Hazards (2024)
Closed Access | Times Cited: 4
Improving the interpretability and predictive power of hydrological models: Applications for daily streamflow in managed and unmanaged catchments
Pravin Bhasme, Udit Bhatia
Journal of Hydrology (2023) Vol. 628, pp. 130421-130421
Closed Access | Times Cited: 11
Monthly Runoff Prediction by Combined Models Based on Secondary Decomposition at the Wulong Hydrological Station in the Yangtze River Basin
Huaibin Wei, Yao Wang, Jing Liu, et al.
Water (2023) Vol. 15, Iss. 21, pp. 3717-3717
Open Access | Times Cited: 7
Contribution to advancing aquifer geometric mapping using machine learning and deep learning techniques: a case study of the AL Haouz-Mejjate aquifer, Marrakech, Morocco
Lhoussaine El Mezouary, Abdessamad Hadri, Mohamed Hakim Kharrou, et al.
Applied Water Science (2024) Vol. 14, Iss. 5
Open Access | Times Cited: 2
Convergent and transdisciplinary integration: On the future of integrated modeling of human-water systems
Saman Razavi, Ashleigh Duffy, Leila Eamen, et al.
Authorea (Authorea) (2024)
Open Access | Times Cited: 1
Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability
Ali El Bilali, Youssef Brouziyne, Oumaima Attar, et al.
Environmental Science and Pollution Research (2024) Vol. 31, Iss. 34, pp. 47237-47257
Closed Access | Times Cited: 1
What controls hydrology? An assessment across the contiguous United States through an interpretable machine learning approach
Kailong Li, Saman Razavi
Journal of Hydrology (2024) Vol. 642, pp. 131835-131835
Closed Access | Times Cited: 1
Data-Driven and Knowledge-Guided Heterogeneous Graphs and Temporal Convolution Networks for Flood Forecasting
Pingping Shao, Jun Feng, Yirui Wu, et al.
Applied Sciences (2023) Vol. 13, Iss. 12, pp. 7191-7191
Open Access | Times Cited: 3
Exploring the power of data-driven models for groundwater system conceptualization: a case study of the Grazer Feld Aquifer, Austria
Ainur Kokimova, Raoul Collenteur, Steffen Birk
Hydrogeology Journal (2024)
Open Access
Net Anthropogenic Nitrogen Input and Its Relationship with Riverine Nitrogen Flux in a Typical Irrigated Area of China Based on an Improved NANI Budgeting Model
Tiezhu Yan, Jianwen Bai, Han Bao, et al.
Water (2023) Vol. 15, Iss. 2, pp. 276-276
Open Access | Times Cited: 1
Leveraging Contextual Cues from a Conceptual Model with Predictive Skills of Machine Learning for Improved Predictability and Interpretability in the Hydrological Processes
Pravin Bhasme, Udit Bhatia
Authorea (Authorea) (2023)
Open Access
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