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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:

State-of-the-Art Development of Two-Waves Artificial Intelligence Modeling Techniques for River Streamflow Forecasting
Woon Yang Tan, Sai Hin Lai, Fang Yenn Teo, et al.
Archives of Computational Methods in Engineering (2022) Vol. 29, Iss. 7, pp. 5185-5211
Closed Access | Times Cited: 10

Showing 10 citing articles:

A review of hybrid deep learning applications for streamflow forecasting
Kin‐Wang Ng, Yuk Feng Huang, Chai Hoon Koo, et al.
Journal of Hydrology (2023) Vol. 625, pp. 130141-130141
Closed Access | Times Cited: 75
Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions
Tao Hai, Sani I. Abba, Ahmed M. Al‐Areeq, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 129, pp. 107559-107559
Closed Access | Times Cited: 57
A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management
Maria Drogkoula, Konstantinos Kokkinos, Nicholas Samaras
Applied Sciences (2023) Vol. 13, Iss. 22, pp. 12147-12147
Open Access | Times Cited: 47
Using Adaptive Chaotic Grey Wolf Optimization for the daily streamflow prediction
Jing Liang, Yukun Du, Yi‐Peng Xu, et al.
Expert Systems with Applications (2023) Vol. 237, pp. 121113-121113
Closed Access | Times Cited: 13
High-Resolution Flow and Phosphorus Forecasting Using ANN Models, Catering for Extremes in the Case of the River Swale (UK)
Elisabeta Cristina Timiș, Horia Hangan, Mircea Vasile Cristea, et al.
Hydrology (2025) Vol. 12, Iss. 2, pp. 20-20
Open Access
Assessing the effectiveness of artificial intelligence approaches for streamflow modeling in the Indravathi subbasin, India
Subbarayan Saravanan, Nagireddy Masthan Reddy
Environment Development and Sustainability (2025)
Closed Access
Multi-phase hybrid bidirectional deep learning model integrated with Markov chain Monte Carlo bivariate copulas function for streamflow prediction
Asif Iqbal, Tanveer Ahmed Siddiqi
Stochastic Environmental Research and Risk Assessment (2023) Vol. 38, Iss. 4, pp. 1351-1382
Closed Access | Times Cited: 4
High-performance prediction model combining minimum redundancy maximum relevance, circulant spectrum analysis, and machine learning methods for daily and peak streamflow
Levent Latifoğlu, Esra Kaya
Theoretical and Applied Climatology (2023) Vol. 155, Iss. 1, pp. 621-643
Closed Access | Times Cited: 2
Data‐driven artificial intelligence‐based streamflow forecasting, a review of methods, applications, and tools
Heerbod Jahanbani, Khandakar Ahmed, Bruce Gu
JAWRA Journal of the American Water Resources Association (2024)
Open Access
Groundwater Level Forecasting Using Empirical Mode Decomposition and Wavelet-Based Long Short-Term Memory (LSTM) Neural Networks
Amirhossein Nazari, M Jamshidi, Abbas Roozbahani, et al.
Groundwater for Sustainable Development (2024), pp. 101397-101397
Open Access
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