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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:

Comparing a long short-term memory (LSTM) neural network with a physically-based hydrological model for streamflow forecasting over a Canadian catchment
Behmard Sabzipour, Richard Arsenault, Magali Troin, et al.
Journal of Hydrology (2023) Vol. 627, pp. 130380-130380
Open Access | Times Cited: 44

Showing 1-25 of 44 citing articles:

Assessing the performance and interpretability of the CNN-LSTM-Attention model for daily streamflow forecasting in typical basins of the eastern Qinghai-Tibet Plateau
Chuchu Zhang, Yuyan Zhou, Lu Fan, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Deep learning model based on coupled SWAT and interpretable methods for water quality prediction under the influence of non-point source pollution
Juan Huan, Yixiong Fan, Xiangen Xu, et al.
Computers and Electronics in Agriculture (2025) Vol. 231, pp. 109985-109985
Closed Access | Times Cited: 1
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: 12
Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments
Kai Ma, Daming He, Shiyin Liu, et al.
Journal of Hydrology (2024) Vol. 631, pp. 130841-130841
Open Access | Times Cited: 9
Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets
F. M. Hasan, Paul Medley, Jason Drake, et al.
Water (2024) Vol. 16, Iss. 13, pp. 1904-1904
Open Access | Times Cited: 7
Enhancing Soil Moisture Forecasting Accuracy with REDF-LSTM: Integrating Residual En-Decoding and Feature Attention Mechanisms
Xiaoning Li, Ziyin Zhang, Qingliang Li, et al.
Water (2024) Vol. 16, Iss. 10, pp. 1376-1376
Open Access | Times Cited: 6
Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models
Soobin Kim, Eunhee Lee, Hyoun‐Tae Hwang, et al.
Water Research X (2024) Vol. 23, pp. 100228-100228
Open Access | Times Cited: 6
Research on runoff process vectorization and integration of deep learning algorithms for flood forecasting
Chengshuai Liu, Wenzhong Li, Caihong Hu, et al.
Journal of Environmental Management (2024) Vol. 362, pp. 121260-121260
Closed Access | Times Cited: 5
Research on machine learning hybrid framework by coupling grid-based runoff generation model and runoff process vectorization for flood forecasting
Chengshuai Liu, Tianning Xie, Wenzhong Li, et al.
Journal of Environmental Management (2024) Vol. 364, pp. 121466-121466
Closed Access | Times Cited: 5
VMDI-LSTM-ED: A novel enhanced decomposition ensemble model incorporating data integration for accurate non-stationary daily streamflow forecasting
Jiadong Liu, Teng Xu, Chunhui Lu
Journal of Hydrology (2025), pp. 132769-132769
Closed Access
Data-driven model as a post-process for daily streamflow prediction in ungauged basins
Jeonghyeon Choi, Sangdan Kim
Heliyon (2025) Vol. 11, Iss. 4, pp. e42512-e42512
Open Access
A novel deep learning-based approach for reconstruction of historical long-term high-quality gridded meteorological dataset
Yookyung Jeong, Dongkyun Kim, Kyuhyun Byun
Journal of Hydrology (2025), pp. 132850-132850
Closed Access
Improving trans-regional hydrological modelling by combining LSTM with big hydrological data
Senlin Tang, Fubao Sun, Qiang Zhang, et al.
Journal of Hydrology Regional Studies (2025) Vol. 58, pp. 102257-102257
Closed Access
Improving multi-model ensemble streamflow forecasts by combining lumped, distributed and deep learning hydrological models
William F. Armstrong, Richard Arsenault, Jean‐Luc Martel, et al.
Hydrological Sciences Journal (2025)
Closed Access
Research on a hybrid model for flood probability prediction based on time convolutional network and particle swarm optimization algorithm
Qiying Yu, Chengshuai Liu, Runxi Li, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
CA-LSTM: Unit Operational State Prediction Based on Multi-head Attention Mechanism and Sequence Modeling
Xingyuan Li, Jianzhuo Yan, Yongchuan Yu
Lecture notes in electrical engineering (2025), pp. 311-322
Closed Access
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: 4
Improving Stream Solute Predictions With a Modified LSTM Model Incorporating Solute Interdependences and Hysteresis Patterns
Tarun Agrawal, Allison E. Goodwell, Praveen Kumar
Journal of Geophysical Research Machine Learning and Computation (2025) Vol. 2, Iss. 1
Open Access
Streamflow Prediction with Time-Lag-Informed Random Forest and Its Performance Compared to SWAT in Diverse Catchments
Desalew Meseret Moges, Holger Virro, Alexander Kmoch, et al.
Water (2024) Vol. 16, Iss. 19, pp. 2805-2805
Open Access | Times Cited: 3
Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder
Mohammad Sina Jahangir, John Quilty
Journal of Hydrology (2023) Vol. 629, pp. 130498-130498
Closed Access | Times Cited: 9
Daily Runoff Prediction Based on FA-LSTM Model
Qihui Chai, Shuting Zhang, Qingqing Tian, et al.
Water (2024) Vol. 16, Iss. 16, pp. 2216-2216
Open Access | Times Cited: 3
A Time Series Forecasting Approach Based on Meta-Learning for Petroleum Production under Few-Shot Samples
Zhichao Xu, Gaoming Yu
Energies (2024) Vol. 17, Iss. 8, pp. 1947-1947
Open Access | Times Cited: 2
Investigating the potential of EMA-embedded feature selection method for ESVR and LSTM to enhance the robustness of monthly streamflow forecasting from local meteorological information
Lei Xu, Peng Shi, Hongshi Wu, et al.
Journal of Hydrology (2024) Vol. 636, pp. 131230-131230
Closed Access | Times Cited: 2
CNN vs. LSTM: A Comparative Study of Hourly Precipitation Intensity Prediction as a Key Factor in Flood Forecasting Frameworks
Isa Ebtehaj, Hossein Bonakdari
Atmosphere (2024) Vol. 15, Iss. 9, pp. 1082-1082
Open Access | Times Cited: 2
Evaluating the impact of improved filter-wrapper input variable selection on Long-term runoff forecasting using local and global climate information
Binlin Yang, Chen Lu, Bin Yi, et al.
Journal of Hydrology (2024), pp. 132034-132034
Closed Access | Times Cited: 2
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