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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:

Improving monthly streamflow prediction in alpine regions: integrating HBV model with Bayesian neural network
Weiwei Ren, Tao Yang, Ching‐Sheng Huang, et al.
Stochastic Environmental Research and Risk Assessment (2018) Vol. 32, Iss. 12, pp. 3381-3396
Closed Access | Times Cited: 47

Showing 1-25 of 47 citing articles:

Machine learning for hydrologic sciences: An introductory overview
Tianfang Xu, Feng Liang
Wiley Interdisciplinary Reviews Water (2021) Vol. 8, Iss. 5
Closed Access | Times Cited: 148
An ensemble CNN-LSTM and GRU adaptive weighting model based improved sparrow search algorithm for predicting runoff using historical meteorological and runoff data as input
Zhiyuan Yao, Zhaocai Wang, Dangwei Wang, et al.
Journal of Hydrology (2023) Vol. 625, pp. 129977-129977
Closed Access | Times Cited: 92
Hybrid forecasting: blending climate predictions with AI models
Louise Slater, Louise Arnal, Marie‐Amélie Boucher, et al.
Hydrology and earth system sciences (2023) Vol. 27, Iss. 9, pp. 1865-1889
Open Access | Times Cited: 84
Runoff Forecasting using Convolutional Neural Networks and optimized Bi-directional Long Short-term Memory
Tunhua Wu, Zhaocai Wang, Yuan Hu, et al.
Water Resources Management (2023) Vol. 37, Iss. 2, pp. 937-953
Closed Access | Times Cited: 76
Glacio‐hydrological model calibration and evaluation
Marit Van Tiel, Kerstin Stahl, Daphné Freudiger, et al.
Wiley Interdisciplinary Reviews Water (2020) Vol. 7, Iss. 6
Open Access | Times Cited: 87
Embedding machine learning techniques into a conceptual model to improve monthly runoff simulation: A nested hybrid rainfall-runoff modeling
Umut Okkan, Zeynep Beril Ersoy, Ahmet Ali Kumanlıoğlu, et al.
Journal of Hydrology (2021) Vol. 598, pp. 126433-126433
Closed Access | Times Cited: 67
A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy
Abhinanda Roy, K. S. Kasiviswanathan, Sandhya Patidar, et al.
Water Resources Research (2023) Vol. 59, Iss. 2
Closed Access | Times Cited: 24
Enhancing streamflow estimation by integrating a data-driven evapotranspiration submodel into process-based hydrological models
Lian Xie, Xiaolong Hu, Jiang Bian, et al.
Journal of Hydrology (2023) Vol. 621, pp. 129603-129603
Closed Access | Times Cited: 22
Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review
Bisrat Ayalew Yifru, Kyoung Jae Lim, Seoro Lee
Sustainability (2024) Vol. 16, Iss. 4, pp. 1376-1376
Open Access | Times Cited: 9
A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting
Zhen Cui, Yanlai Zhou, Shenglian Guo, et al.
Hydrology Research (2021) Vol. 52, Iss. 6, pp. 1436-1454
Open Access | Times Cited: 48
A stochastic conceptual-data-driven approach for improved hydrological simulations
John Quilty, Anna E. Sikorska‐Senoner, David Hah
Environmental Modelling & Software (2022) Vol. 149, pp. 105326-105326
Open Access | Times Cited: 35
Evaluation of the impact of climate change on the streamflow of major pan-Arctic river basins through machine learning models
Shuyu Zhang, Thian Yew Gan, Andrew B. G. Bush, et al.
Journal of Hydrology (2023) Vol. 619, pp. 129295-129295
Closed Access | Times Cited: 19
Comparison of Machine Learning Models in Simulating Glacier Mass Balance: Insights from Maritime and Continental Glaciers in High Mountain Asia
Weiwei Ren, Zhongzheng Zhu, Yingzheng Wang, et al.
Remote Sensing (2024) Vol. 16, Iss. 6, pp. 956-956
Open Access | Times Cited: 6
A hybrid model coupling process-driven and data-driven models for improved real-time flood forecasting
Chengjing Xu, Ping‐an Zhong, Feilin Zhu, et al.
Journal of Hydrology (2024) Vol. 638, pp. 131494-131494
Closed Access | Times Cited: 5
Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models
Louise Slater, Louise Arnal, Marie‐Amélie Boucher, et al.
(2022)
Open Access | Times Cited: 23
Spatial modeling of river bank shifting and associated LULC changes of the Kaljani River in Himalayan foothills
Md Hasanuzzaman, Amiya Gayen, Sk. Mafizul Haque, et al.
Stochastic Environmental Research and Risk Assessment (2022) Vol. 36, Iss. 2, pp. 563-582
Open Access | Times Cited: 22
Enhancing Flood Simulation in Data-Limited Glacial River Basins through Hybrid Modeling and Multi-Source Remote Sensing Data
Weiwei Ren, Xin Li, Donghai Zheng, et al.
Remote Sensing (2023) Vol. 15, Iss. 18, pp. 4527-4527
Open Access | Times Cited: 13
Evaluating the migration of boundary river shorelines and coastal land cover changes for the Beilun River between China and Vietnam
Xie Shuangmi, Zhang Wenzhu, Wu Bin, et al.
Journal of Hydrology Regional Studies (2025) Vol. 57, pp. 102167-102167
Closed Access
Coupling the Xinanjiang model and wavelet-based random forests method for improved daily streamflow simulation
Wang Jian, Weimin Bao, Qianyu Gao, et al.
Journal of Hydroinformatics (2021) Vol. 23, Iss. 3, pp. 589-604
Open Access | Times Cited: 28
Untangling hybrid hydrological models with explainable artificial intelligence
Daniel Althoff, Helizani Couto Bazame, Jéssica Garcia Nascimento
H2Open Journal (2021) Vol. 4, Iss. 1, pp. 13-28
Open Access | Times Cited: 27
Physical and artificial intelligence-based hybrid models for rainfall–runoff–sediment process modelling
Gebre Gelete, Vahid Nourani, Hüseyin Gökçekuş, et al.
Hydrological Sciences Journal (2023) Vol. 68, Iss. 13, pp. 1841-1863
Closed Access | Times Cited: 10
A Physics‐Aware Machine Learning‐Based Framework for Minimizing Prediction Uncertainty of Hydrological Models
Abhinanda Roy, K. S. Kasiviswanathan, Sandhya Patidar, et al.
Water Resources Research (2023) Vol. 59, Iss. 6
Closed Access | Times Cited: 9
An integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis method for quantifying climate change impact on runoff of the Amu Darya River basin
Y.Y. Su, Yongping Li, Y.R. Liu, et al.
International Journal of Climatology (2021) Vol. 41, Iss. 5, pp. 3411-3424
Closed Access | Times Cited: 22
Performance evaluation of various hydrological models with respect to hydrological responses under climate change scenario: a review
Yilak Taye Bihon, Tarun Kumar Lohani, Abebe Temesgen Ayalew, et al.
Cogent Engineering (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 2
Monthly streamflow prediction using a hybrid stochastic-deterministic approach for parsimonious non-linear time series modeling
Zhen Wang, Nasrin Fathollahzadeh Attar, Keivan Khalili, et al.
Engineering Applications of Computational Fluid Mechanics (2020) Vol. 14, Iss. 1, pp. 1351-1372
Open Access | Times Cited: 20
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