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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:

Long lead-time daily and monthly streamflow forecasting using machine learning methods
Meiling Cheng, F. Fang, Tsuyoshi Kinouchi, et al.
Journal of Hydrology (2020) Vol. 590, pp. 125376-125376
Closed Access | Times Cited: 229

Showing 1-25 of 229 citing articles:

Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting
Maryam Rahimzad, Alireza Moghaddam Nia, Hesam Zolfonoon, et al.
Water Resources Management (2021) Vol. 35, Iss. 12, pp. 4167-4187
Closed Access | Times Cited: 163
Coupling a hybrid CNN-LSTM deep learning model with a Boundary Corrected Maximal Overlap Discrete Wavelet Transform for multiscale Lake water level forecasting
Rahim Barzegar, Mohammad Taghi Aalami, Jan Adamowski
Journal of Hydrology (2021) Vol. 598, pp. 126196-126196
Closed Access | Times Cited: 155
Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions
Halit Apaydın, Mohammad Taghi Sattari, Kambiz Falsafian, et al.
Journal of Hydrology (2021) Vol. 600, pp. 126506-126506
Closed Access | Times Cited: 112
The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction
Rana Muhammad Adnan Ikram, Ahmed A. Ewees, Kulwinder Singh Parmar, et al.
Applied Soft Computing (2022) Vol. 131, pp. 109739-109739
Closed Access | Times Cited: 112
Stacked machine learning algorithms and bidirectional long short-term memory networks for multi-step ahead streamflow forecasting: A comparative study
Francesco Granata, Fabio Di Nunno, Giovanni de Marinis
Journal of Hydrology (2022) Vol. 613, pp. 128431-128431
Closed Access | Times Cited: 106
The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management
Vijendra Kumar, Hazi Mohammad Azamathulla, Kul Vaibhav Sharma, et al.
Sustainability (2023) Vol. 15, Iss. 13, pp. 10543-10543
Open Access | Times Cited: 100
Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change
Sungwook Wi, Scott Steinschneider
Water Resources Research (2022) Vol. 58, Iss. 9
Closed Access | Times Cited: 85
Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for hydrological processes
Pravin Bhasme, Jenil Vagadiya, Udit Bhatia
Journal of Hydrology (2022) Vol. 615, pp. 128618-128618
Open Access | Times Cited: 82
Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems
Erdal Uncuoğlu, Hatice Çıtakoğlu, Levent Latifoğlu, et al.
Applied Soft Computing (2022) Vol. 129, pp. 109623-109623
Closed Access | Times Cited: 79
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: 77
Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling
Keighobad Jafarzadegan, Hamid Moradkhani, Florian Pappenberger, et al.
Reviews of Geophysics (2023) Vol. 61, Iss. 2
Open Access | Times Cited: 68
Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran
Mohammad Akbarian, Bahram Saghafian, Saeed Golian
Journal of Hydrology (2023) Vol. 620, pp. 129480-129480
Closed Access | Times Cited: 67
Integrating Machine Learning and AI for Improved Hydrological Modeling and Water Resource Management
Djabeur Mohamed Seifeddine Zekrifa, Megha Kulkarni, A. Bhagyalakshmi, et al.
Advances in environmental engineering and green technologies book series (2023), pp. 46-70
Closed Access | Times Cited: 59
Neuroforecasting of daily streamflows in the UK for short- and medium-term horizons: A novel insight
Francesco Granata, Fabio Di Nunno
Journal of Hydrology (2023) Vol. 624, pp. 129888-129888
Closed Access | Times Cited: 57
Applications of machine learning to water resources management: A review of present status and future opportunities
Ashraf Ahmed, Sakina Sayed, Antoifi Abdoulhalik, et al.
Journal of Cleaner Production (2024) Vol. 441, pp. 140715-140715
Open Access | Times Cited: 53
Coupling SWAT and Bi-LSTM for improving daily-scale hydro-climatic simulation and climate change impact assessment in a tropical river basin
Shuai Yang, Mou Leong Tan, Qixuan Song, et al.
Journal of Environmental Management (2023) Vol. 330, pp. 117244-117244
Closed Access | Times Cited: 46
Groundwater level prediction using an improved SVR model integrated with hybrid particle swarm optimization and firefly algorithm
Sandeep Samantaray, Abinash Sahoo, Falguni Baliarsingh
Cleaner Water (2024) Vol. 1, pp. 100003-100003
Open Access | Times Cited: 32
Modeling streamflow in non-gauged watersheds with sparse data considering physiographic, dynamic climate, and anthropogenic factors using explainable soft computing techniques
Charuni I. Madhushani, K. G. S. Dananjaya, I.U. Ekanayake, et al.
Journal of Hydrology (2024) Vol. 631, pp. 130846-130846
Closed Access | Times Cited: 24
A new frontier in streamflow modeling in ungauged basins with sparse data: A modified generative adversarial network with explainable AI
U.A.K.K. Perera, D.T.S. Coralage, I.U. Ekanayake, et al.
Results in Engineering (2024) Vol. 21, pp. 101920-101920
Open Access | Times Cited: 17
Coupling Deep Learning and Physically Based Hydrological Models for Monthly Streamflow Predictions
Wenxin Xu, Jie Chen, Gerald Corzo, et al.
Water Resources Research (2024) Vol. 60, Iss. 2
Open Access | Times Cited: 17
Daily runoff forecasting by deep recursive neural network
Jiang-Wei Zhang, Xiaohui Chen, Amirul Khan, et al.
Journal of Hydrology (2021) Vol. 596, pp. 126067-126067
Closed Access | Times Cited: 96
A comprehensive assessment of water storage dynamics and hydroclimatic extremes in the Chao Phraya River Basin during 2002–2020
Abhishek Abhishek, Tsuyoshi Kinouchi, Takahiro Sayama
Journal of Hydrology (2021) Vol. 603, pp. 126868-126868
Open Access | Times Cited: 83
Prediction of Yangtze River streamflow based on deep learning neural network with El Niño–Southern Oscillation
Si Ha, Darong Liu, Lin Mu
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 74
Development of new machine learning model for streamflow prediction: case studies in Pakistan
Rana Muhammad Adnan, Reham R. Mostafa, Ahmed Elbeltagi, et al.
Stochastic Environmental Research and Risk Assessment (2021) Vol. 36, Iss. 4, pp. 999-1033
Closed Access | Times Cited: 63
Spatial-temporal flood inundation nowcasts by fusing machine learning methods and principal component analysis
Li‐Chiu Chang, Jia-Yi Liou, Fi‐John Chang
Journal of Hydrology (2022) Vol. 612, pp. 128086-128086
Closed Access | Times Cited: 59
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