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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:

Real-time rainfall-runoff prediction using light gradient boosting machine coupled with singular spectrum analysis
Zhongjie Cui, Qing Xiaoxia, Hongxiang Chai, et al.
Journal of Hydrology (2021) Vol. 603, pp. 127124-127124
Closed Access | Times Cited: 67

Showing 1-25 of 67 citing articles:

Prediction of heating and cooling loads based on light gradient boosting machine algorithms
Jiaxin Guo, Sining Yun, Yao Meng, et al.
Building and Environment (2023) Vol. 236, pp. 110252-110252
Closed Access | Times Cited: 65
Application, interpretability and prediction of machine learning method combined with LSTM and LightGBM-a case study for runoff simulation in an arid area
Ling-Zhu Bian, Xueer Qin, Chenglong Zhang, et al.
Journal of Hydrology (2023) Vol. 625, pp. 130091-130091
Closed Access | Times Cited: 45
Rapid Prediction Model for Urban Floods Based on a Light Gradient Boosting Machine Approach and Hydrological–Hydraulic Model
Kui Xu, Zhentao Han, Hongshi Xu, et al.
International Journal of Disaster Risk Science (2023)
Open Access | Times Cited: 42
Ensemble learning using multivariate variational mode decomposition based on the Transformer for multi-step-ahead streamflow forecasting
Jinjie Fang, Linshan Yang, Xiaohu Wen, et al.
Journal of Hydrology (2024) Vol. 636, pp. 131275-131275
Closed Access | Times Cited: 17
Reconstructing Long-Term Daily Streamflow Data at the Discontinuous Monitoring Station in the Ungauged Transboundary Basin Using Machine Learning
Vinh Ngoc Tran, Thi Thuy Hang Nguyen, Hai Van Khuong, et al.
Water Resources Management (2025)
Closed Access | Times Cited: 2
Daily Streamflow Forecasting in Mountainous Catchment Using XGBoost, LightGBM and CatBoost
Robert Szczepanek
Hydrology (2022) Vol. 9, Iss. 12, pp. 226-226
Open Access | Times Cited: 68
A Review on Interpretable and Explainable Artificial Intelligence in Hydroclimatic Applications
Hakan Başağaoğlu, Debaditya Chakraborty, Cesar Do Lago, et al.
Water (2022) Vol. 14, Iss. 8, pp. 1230-1230
Open Access | Times Cited: 55
A new hybrid model for monthly runoff prediction using ELMAN neural network based on decomposition-integration structure with local error correction method
Dongmei Xu, Xiao-xue Hu, Wenchuan Wang, et al.
Expert Systems with Applications (2023) Vol. 238, pp. 121719-121719
Closed Access | Times Cited: 36
Ultra-short-term Interval Prediction of Wind Power Based on Graph Neural Network and Improved Bootstrap Technique
Wenlong Liao, Shouxiang Wang, Birgitte Bak‐Jensen, et al.
Journal of Modern Power Systems and Clean Energy (2023) Vol. 11, Iss. 4, pp. 1100-1114
Open Access | Times Cited: 34
A conceptual metaheuristic-based framework for improving runoff time series simulation in glacierized catchments
Babak Mohammadi, Saeed Vazifehkhah, Zheng Duan
Engineering Applications of Artificial Intelligence (2023) Vol. 127, pp. 107302-107302
Open Access | Times Cited: 27
Application of a New Hybrid Deep Learning Model That Considers Temporal and Feature Dependencies in Rainfall–Runoff Simulation
Feng Zhou, Yangbo Chen, Jun Liu
Remote Sensing (2023) Vol. 15, Iss. 5, pp. 1395-1395
Open Access | Times Cited: 24
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: 15
Weather and the City: Machine Learning for Predicting and Attributing Fine Scale Air Quality to Meteorological and Urban Determinants
Firas Gerges, Maider Llaguno-Munitxa, Mark A. Zondlo, et al.
Environmental Science & Technology (2024) Vol. 58, Iss. 14, pp. 6313-6325
Closed Access | Times Cited: 9
Urban real-time rainfall-runoff prediction using adaptive SSA-decomposition with dual attention
Yuan Tian, Weiming Fu, Yi Xiang, et al.
Journal of Hydrology (2025), pp. 132701-132701
Closed Access | Times Cited: 1
Translating street view imagery to correct perspectives to enhance bikeability and walkability studies
Koichi Ito, Matías Quintana, Xianjing Han, et al.
International Journal of Geographical Information Science (2024) Vol. 38, Iss. 12, pp. 2514-2544
Closed Access | Times Cited: 8
A state-of-the-art review of long short-term memory models with applications in hydrology and water resources
Zhong-kai Feng, J. Zhang, Wen-jing Niu
Applied Soft Computing (2024), pp. 112352-112352
Closed Access | Times Cited: 8
Employing hybrid deep learning for near-real-time forecasts of sensor-based algal parameters in a Microcystis bloom-dominated lake
Lan Wang, Kun Shan, Yi Yang, et al.
The Science of The Total Environment (2024) Vol. 922, pp. 171009-171009
Closed Access | Times Cited: 7
Generalized singular spectrum analysis for the decomposition and analysis of non-stationary signals
Jialiang Gu, Kevin Hung, Bingo Wing‐Kuen Ling, et al.
Journal of the Franklin Institute (2024) Vol. 361, Iss. 6, pp. 106696-106696
Closed Access | Times Cited: 7
A comprehensive framework for assessing the spatial drivers of flood disasters using an optimal Parameter-based geographical Detector–machine learning coupled model
Luyi Yang, Xuan Ji, Meng Li, et al.
Geoscience Frontiers (2024) Vol. 15, Iss. 6, pp. 101889-101889
Open Access | Times Cited: 7
Multi-step ahead forecasting for electric power load using an ensemble model
Yubo Zhao, Ni Guo, Wei Chen, et al.
Expert Systems with Applications (2022) Vol. 211, pp. 118649-118649
Closed Access | Times Cited: 24
Snow avalanche susceptibility mapping from tree-based machine learning approaches in ungauged or poorly-gauged regions
Yang Liu, Xi Chen, Jinming Yang, et al.
CATENA (2023) Vol. 224, pp. 106997-106997
Closed Access | Times Cited: 16
Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland
Hao Wang, Wei Shao, Dafang Zhuang, et al.
Remote Sensing (2023) Vol. 15, Iss. 14, pp. 3475-3475
Open Access | Times Cited: 15
South America Seasonal Precipitation Prediction by Gradient-Boosting Machine-Learning Approach
Vinicius Schmidt Monego, Juliana Aparecida Anochi, Haroldo Fraga de Campos Velho
Atmosphere (2022) Vol. 13, Iss. 2, pp. 243-243
Open Access | Times Cited: 22
Comparisons of Different Machine Learning-Based Rainfall–Runoff Simulations under Changing Environments
Chenliang Li, Ying Jiao, Guangyuan Kan, et al.
Water (2024) Vol. 16, Iss. 2, pp. 302-302
Open Access | Times Cited: 4
Advances in land surface forecasting: a comparison of LSTM, gradient boosting, and feed-forward neural networks as prognostic state emulators in a case study with ecLand
Marieke Wesselkamp, Matthew Chantry, Ewan Pinnington, et al.
Geoscientific model development (2025) Vol. 18, Iss. 4, pp. 921-937
Open Access
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