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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 the prediction accuracy of monthly streamflow using a data-driven model based on a double-processing strategy
Lili Wang, Xin Li, Chunfeng Ma, et al.
Journal of Hydrology (2019) Vol. 573, pp. 733-745
Closed Access | Times Cited: 68

Showing 1-25 of 68 citing articles:

Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs
Rana Muhammad Adnan, Zhongmin Liang, Salim Heddam, et al.
Journal of Hydrology (2019) Vol. 586, pp. 124371-124371
Closed Access | Times Cited: 231
Streamflow forecasting using extreme gradient boosting model coupled with Gaussian mixture model
Lingling Ni, Dong Wang, Jianfeng Wu, et al.
Journal of Hydrology (2020) Vol. 586, pp. 124901-124901
Closed Access | Times Cited: 211
Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization
Zhong-kai Feng, Wen-jing Niu, Zhengyang Tang, et al.
Journal of Hydrology (2020) Vol. 583, pp. 124627-124627
Closed Access | Times Cited: 210
Merging multiple satellite-based precipitation products and gauge observations using a novel double machine learning approach
Ling Zhang, Xin Li, Donghai Zheng, et al.
Journal of Hydrology (2021) Vol. 594, pp. 125969-125969
Closed Access | Times Cited: 140
An enhanced monthly runoff time series prediction using extreme learning machine optimized by salp swarm algorithm based on time varying filtering based empirical mode decomposition
Wenchuan Wang, Qi Cheng, Kwok‐wing Chau, et al.
Journal of Hydrology (2023) Vol. 620, pp. 129460-129460
Closed Access | Times Cited: 42
Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States
Peiman Parisouj, Hamid Mohebzadeh, Taesam Lee
Water Resources Management (2020) Vol. 34, Iss. 13, pp. 4113-4131
Closed Access | Times Cited: 133
The Applicability of LSTM-KNN Model for Real-Time Flood Forecasting in Different Climate Zones in China
Moyang Liu, Yingchun Huang, Zhijia Li, et al.
Water (2020) Vol. 12, Iss. 2, pp. 440-440
Open Access | Times Cited: 95
Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models
Babak Mohammadi, Roozbeh Moazenzadeh, Kevin Christian, et al.
Environmental Science and Pollution Research (2021) Vol. 28, Iss. 46, pp. 65752-65768
Closed Access | Times Cited: 74
Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach
Rana Muhammad Adnan, Andrea Petroselli‬, Salim Heddam, et al.
Natural Hazards (2021) Vol. 105, Iss. 3, pp. 2987-3011
Closed Access | Times Cited: 69
Evolutionary artificial intelligence model via cooperation search algorithm and extreme learning machine for multiple scales nonstationary hydrological time series prediction
Zhong-kai Feng, Wen-jing Niu, Zhengyang Tang, et al.
Journal of Hydrology (2021) Vol. 595, pp. 126062-126062
Closed Access | Times Cited: 67
Enhancing robustness of monthly streamflow forecasting model using gated recurrent unit based on improved grey wolf optimizer
Xuehua Zhao, Hanfang Lv, Lv Shujin, et al.
Journal of Hydrology (2021) Vol. 601, pp. 126607-126607
Closed Access | Times Cited: 65
Development of new machine learning model for streamflow prediction: case studies in Pakistan
Rana Muhammad Adnan, Reham R. Mostafa, Ahmed Elbeltagi, et al.
Stochastic Environmental Research and Risk Assessment (2021) Vol. 36, Iss. 4, pp. 999-1033
Closed Access | Times Cited: 63
Enhanced LSTM Model for Daily Runoff Prediction in the Upper Huai River Basin, China
Yuanyuan Man, Qinli Yang, Junming Shao, et al.
Engineering (2022) Vol. 24, pp. 229-238
Open Access | Times Cited: 42
An ensemble model for monthly runoff prediction using least squares support vector machine based on variational modal decomposition with dung beetle optimization algorithm and error correction strategy
Dongmei Xu, Zong Li, Wenchuan Wang
Journal of Hydrology (2023) Vol. 629, pp. 130558-130558
Closed Access | Times Cited: 36
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
A new soft computing model for daily streamflow forecasting
Saad Sh. Sammen, Mohammad Ehteram, Sani I. Abba, et al.
Stochastic Environmental Research and Risk Assessment (2021) Vol. 35, Iss. 12, pp. 2479-2491
Closed Access | Times Cited: 48
Random forest and extreme gradient boosting algorithms for streamflow modeling using vessel features and tree-rings
Hossein Sahour, Vahid Gholami, Javad Torkaman, et al.
Environmental Earth Sciences (2021) Vol. 80, Iss. 22
Open Access | Times Cited: 48
Performance improvement of machine learning models via wavelet theory in estimating monthly river streamflow
Kegang Wang, Shahab S. Band, Rasoul Ameri, et al.
Engineering Applications of Computational Fluid Mechanics (2022) Vol. 16, Iss. 1, pp. 1833-1848
Open Access | Times Cited: 36
Parallel cooperation search algorithm and artificial intelligence method for streamflow time series forecasting
Zhong-kai Feng, Pengfei Shi, Tao Yang, et al.
Journal of Hydrology (2022) Vol. 606, pp. 127434-127434
Closed Access | Times Cited: 31
A daily carbon emission prediction model combining two-stage feature selection and optimized extreme learning machine
Feng Kong, Jianbo Song, Zhongzhi Yang
Environmental Science and Pollution Research (2022) Vol. 29, Iss. 58, pp. 87983-87997
Closed Access | Times Cited: 28
Application of empirical mode decomposition, particle swarm optimization, and support vector machine methods to predict stream flows
Okan Mert Katipoğlu, Sefa Nur Yeşilyurt, Yıldırım Dalkiliç, et al.
Environmental Monitoring and Assessment (2023) Vol. 195, Iss. 9
Closed Access | Times Cited: 20
Residual energy evaluation in vortex structures: On the application of machine learning models
Mohammad Najafzadeh, Mohammad Mahmoudi-Rad
Results in Engineering (2024) Vol. 23, pp. 102792-102792
Open Access | Times Cited: 6
Fundamental heart sounds analysis using improved complete ensemble EMD with adaptive noise
Miguel Altuve, Luis Fernando Durango Suárez, Jeyson Ardila
Journal of Applied Biomedicine (2020) Vol. 40, Iss. 1, pp. 426-439
Closed Access | Times Cited: 39
An enhanced monthly runoff forecasting using least squares support vector machine based on Harris hawks optimization and secondary decomposition
Dongmei Xu, Xiao-xue Hu, Wenchuan Wang, et al.
Earth Science Informatics (2023) Vol. 16, Iss. 3, pp. 2089-2109
Closed Access | Times Cited: 15
Enhancing accuracy of extreme learning machine in predicting river flow using improved reptile search algorithm
Rana Muhammad Adnan, Reham R. Mostafa, Hongliang Dai, et al.
Stochastic Environmental Research and Risk Assessment (2023) Vol. 37, Iss. 8, pp. 3063-3083
Closed Access | Times Cited: 14
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