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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:

Capability assessment of conventional and data-driven models for prediction of suspended sediment load
Ashish Kumar, Vinod Kumar Tripathi
Environmental Science and Pollution Research (2022) Vol. 29, Iss. 33, pp. 50040-50058
Closed Access | Times Cited: 8

Showing 8 citing articles:

Suspended sediment load prediction using sparrow search algorithm-based support vector machine model
Sandeep Samantaray, Abinash Sahoo, Deba Prakash Satapathy, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 22
Applications and interpretations of different machine learning models in runoff and sediment discharge simulations
Jindian Miao, Xiaoming Zhang, Guojun Zhang, et al.
CATENA (2024) Vol. 238, pp. 107848-107848
Closed Access | Times Cited: 9
Enhancing sediment transport predictions through machine learning-based multi-scenario regression models
Mohammad Abdullah Almubaidin, Sarmad Dashti Latif, Kalaiarasan Balan, et al.
Results in Engineering (2023) Vol. 20, pp. 101585-101585
Open Access | Times Cited: 19
A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration
Bhupendra Joshi, Vijay Kumar Singh, Dinesh Kumar Vishwakarma, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 6
Applying the C-Factor of the RUSLE Model to Improve the Prediction of Suspended Sediment Concentration Using Smart Data-Driven Models
Haniyeh Asadi, Mohammad Taghi Dastorani, Khabat Khosravi, et al.
Water (2022) Vol. 14, Iss. 19, pp. 3011-3011
Open Access | Times Cited: 5
Developing long short-term memory combined with numerical first order differential optimization and clockwork recurrent neural network to predict suspended sediment load
Milad Sharafi, Sadra Shadkani, Amirreza Pak, et al.
Earth Science Informatics (2024) Vol. 18, Iss. 1
Closed Access
Improved quantum artificial bee colony algorithm-optimized artificial intelligence models for suspended sediment load predicting
Peng. Wei, Yu Wang
IEEE Access (2023), pp. 1-1
Open Access | Times Cited: 1
Models for Predicting River Suspended Sediment Load Using Machine Learning: A Survey
Lubna Jamal Chachan, Baydaa Sulaiman Bahnam
Technium Romanian Journal of Applied Sciences and Technology (2022) Vol. 4, Iss. 10, pp. 239-249
Open Access | Times Cited: 2
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