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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:

Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling
Herath Mudiyanselage Viraj Vidura Herath, Jayashree Chadalawada, Vladan Babovic
Hydrology and earth system sciences (2021) Vol. 25, Iss. 8, pp. 4373-4401
Open Access | Times Cited: 133

Showing 1-25 of 133 citing articles:

Deep learning rainfall–runoff predictions of extreme events
Jonathan Frame, Frederik Kratzert, Daniel Klotz, et al.
Hydrology and earth system sciences (2022) Vol. 26, Iss. 13, pp. 3377-3392
Open Access | Times Cited: 204
Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models
Thomas Lees, Marcus Buechel, Bailey Anderson, et al.
Hydrology and earth system sciences (2021) Vol. 25, Iss. 10, pp. 5517-5534
Open Access | Times Cited: 164
The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology
Kuai Fang, Daniel Kifer, Kathryn Lawson, et al.
Water Resources Research (2022) Vol. 58, Iss. 4
Open Access | Times Cited: 131
Study on optimization and combination strategy of multiple daily runoff prediction models coupled with physical mechanism and LSTM
Jun Guo, Yi Liu, Qiang Zou, et al.
Journal of Hydrology (2023) Vol. 624, pp. 129969-129969
Closed Access | Times Cited: 116
Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models
Vijendra Kumar, Naresh Kedam, Kul Vaibhav Sharma, et al.
Water (2023) Vol. 15, Iss. 14, pp. 2572-2572
Open Access | Times Cited: 97
The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL)
Juliane Mai, Hongren Shen, Bryan A. Tolson, et al.
Hydrology and earth system sciences (2022) Vol. 26, Iss. 13, pp. 3537-3572
Open Access | Times Cited: 78
RR-Former: Rainfall-runoff modeling based on Transformer
Hanlin Yin, Zilong Guo, Xiuwei Zhang, et al.
Journal of Hydrology (2022) Vol. 609, pp. 127781-127781
Closed Access | Times Cited: 74
Global prediction of extreme floods in ungauged watersheds
Grey Nearing, Déborah Cohen, Vusumuzi Dube, et al.
Nature (2024) Vol. 627, Iss. 8004, pp. 559-563
Open Access | Times Cited: 73
Differences in extremes and uncertainties in future runoff simulations using SWAT and LSTM for SSP scenarios
Young Hoon Song, ‪Eun‐Sung Chung, Shamsuddin Shahid
The Science of The Total Environment (2022) Vol. 838, pp. 156162-156162
Closed Access | Times Cited: 72
Machine-learning- and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data
Dharmaveer Singh, Manu Vardhan, Rakesh Sahu, et al.
Hydrology and earth system sciences (2023) Vol. 27, Iss. 5, pp. 1047-1075
Open Access | Times Cited: 49
Distributed Hydrological Modeling With Physics‐Encoded Deep Learning: A General Framework and Its Application in the Amazon
Chao Wang, Shijie Jiang, Yi Zheng, et al.
Water Resources Research (2024) Vol. 60, Iss. 4
Open Access | Times Cited: 17
A new seq2seq architecture for hourly runoff prediction using historical rainfall and runoff as input
Shuai Gao, Shuo Zhang, Yuefei Huang, et al.
Journal of Hydrology (2022) Vol. 612, pp. 128099-128099
Closed Access | Times Cited: 37
Development of a TVF-EMD-based multi-decomposition technique integrated with Encoder-Decoder-Bidirectional-LSTM for monthly rainfall forecasting
Mehdi Jamei, Mumtaz Ali, Anurag Malik, et al.
Journal of Hydrology (2023) Vol. 617, pp. 129105-129105
Closed Access | Times Cited: 36
Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model
Junhao Wu, Zhaocai Wang, Jinghan Dong, et al.
Water Resources Research (2023) Vol. 59, Iss. 9
Closed Access | Times Cited: 29
Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test
Dinesh Kumar Vishwakarma, Alban Kuriqi, Salwan Ali Abed, et al.
Heliyon (2023) Vol. 9, Iss. 5, pp. e16290-e16290
Open Access | Times Cited: 27
Application of machine learning and emerging remote sensing techniques in hydrology: A state-of-the-art review and current research trends
Asish Saha, Subodh Chandra Pal
Journal of Hydrology (2024) Vol. 632, pp. 130907-130907
Closed Access | Times Cited: 13
Novel hybrid intelligence predictive model based on successive variational mode decomposition algorithm for monthly runoff series
Abbas Parsaie, Redvan Ghasemlounıa, Amin Gharehbaghi, et al.
Journal of Hydrology (2024) Vol. 634, pp. 131041-131041
Closed Access | Times Cited: 13
Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: a case study of Bino watershed, Uttarakhand
Paramjeet Singh Tulla, Pravendra Kumar, Dinesh Kumar Vishwakarma, et al.
Theoretical and Applied Climatology (2024) Vol. 155, Iss. 5, pp. 4023-4047
Closed Access | Times Cited: 11
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 hybrid deep learning approach for streamflow prediction utilizing watershed memory and process-based modeling
Bisrat Ayalew Yifru, Kyoung Jae Lim, Joo Hyun Bae, et al.
Hydrology Research (2024) Vol. 55, Iss. 4, pp. 498-518
Open Access | Times Cited: 9
A hybrid model enhancing streamflow forecasts in paddy land use-dominated catchments with numerical weather prediction model-based meteorological forcings
Ashrumochan Mohanty, Bhabagrahi Sahoo, Ravindra V. Kale
Journal of Hydrology (2024) Vol. 635, pp. 131225-131225
Closed Access | Times Cited: 9
Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins
Yuanhao Xu, Kairong Lin, Caihong Hu, et al.
Journal of Hydrology (2024) Vol. 639, pp. 131598-131598
Closed 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
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
Benchmarking data-driven rainfall-runoff modeling across 54 catchments in the Yellow River Basin: Overfitting, calibration length, dry frequency
Jin Jin, Yanning Zhang, Zhen Hao, et al.
Journal of Hydrology Regional Studies (2022) Vol. 42, pp. 101119-101119
Open Access | Times Cited: 33
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