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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-term streamflow forecasting in data-scarce regions: Insightful investigation for leveraging satellite-derived data, Informer architecture, and concurrent fine-tuning transfer learning
Fatemeh Ghobadi, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Doosun Kang
Journal of Hydrology (2024) Vol. 631, pp. 130772-130772
Closed Access | Times Cited: 14

Showing 14 citing articles:

Revolutionizing the Future of Hydrological Science: Impact of Machine Learning and Deep Learning amidst Emerging Explainable AI and Transfer Learning
Rajib Maity, Aman Srivastava, Subharthi Sarkar, et al.
Applied Computing and Geosciences (2024) Vol. 24, pp. 100206-100206
Open Access | Times Cited: 5
Deformation mechanism-assisted deep learning architecture for predicting step-like displacement of reservoir landslide
Yanan Jiang, Linfeng Zheng, Qiang Xu, et al.
International Journal of Applied Earth Observation and Geoinformation (2024) Vol. 133, pp. 104121-104121
Open Access | Times Cited: 4
Enhancing Long-Term Flood Forecasting with SageFormer: A Cascaded Dimensionality Reduction Approach Based on Satellite-Derived Data
Fatemeh Ghobadi, Amir Saman Tayerani Charmchi, Doosun Kang
Remote Sensing (2025) Vol. 17, Iss. 3, pp. 365-365
Open Access
Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application
Xueying Chen, Yuhang Zhang, Aizhong Ye, et al.
Environmental Modelling & Software (2025), pp. 106350-106350
Open Access
Mixture of experts leveraging informer and LSTM variants for enhanced daily streamflow forecasting
Zerong Rong, Wei Sun, Yutong Xie, et al.
Journal of Hydrology (2025), pp. 132737-132737
Closed Access
Streamflow Intervals Prediction Using Coupled Autoregressive Conditionally Heteroscedastic With Bootstrap Model
Bugrayhan Bickici, Beste Hamiye Beyaztaş, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, et al.
Journal of Flood Risk Management (2025) Vol. 18, Iss. 1
Open Access
A generalised hydrological model for streamflow prediction using wavelet Ensembling
Chinmaya Panda, Kanhu Charan Panda, Ram Mandir Singh, et al.
Journal of Hydrology (2025), pp. 132883-132883
Closed Access
Enhancing Hydro-climatic and land parameter forecasting using Transformer networks
Suchismita Subhadarsini, D. Nagesh Kumar, Rao S. Govindaraju
Journal of Hydrology (2025), pp. 132906-132906
Closed Access
A Transfer Learning Remote Sensing Landslide Image Segmentation Method Based on Nonlinear Modeling and Large Kernel Attention
Jiajun Li, Qiang Li, Jinzheng Lu, et al.
Applied Sciences (2025) Vol. 15, Iss. 7, pp. 3855-3855
Open Access
An improved graph neural network integrating indicator attention and spatio-temporal correlation for dissolved oxygen prediction
Fei Ding, Shilong Hao, Mingcen Jiang, et al.
Ecological Informatics (2025), pp. 103126-103126
Open Access
Enhanced rainfall nowcasting of tropical cyclone by an interpretable deep learning model and its application in real-time flood forecasting
Li Liu, Xiao Liang, Yue‐Ping Xu, et al.
Journal of Hydrology (2024), pp. 131993-131993
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
ConvFormer-KDE: A Long-Term Point–Interval Prediction Framework for PM2.5 Based on Multi-Source Spatial and Temporal Data
Shaofu Lin, Yuying Zhang, Xingjia Fei, et al.
Toxics (2024) Vol. 12, Iss. 8, pp. 554-554
Open Access
Does grouping watersheds by hydrographic regions offer any advantages in fine-tuning transfer learning model for temporal and spatial streamflow predictions?
Yegane Khoshkalam, Alain N. Rousseau, Farshid Rahmani, et al.
Journal of Hydrology (2024), pp. 132540-132540
Closed Access
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