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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:

A robust method for non-stationary streamflow prediction based on improved EMD-SVM model
Erhao Meng, Shengzhi Huang, Qiang Huang, et al.
Journal of Hydrology (2018) Vol. 568, pp. 462-478
Closed Access | Times Cited: 230

Showing 1-25 of 230 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: 216
Decomposition ensemble model based on variational mode decomposition and long short-term memory for streamflow forecasting
Ganggang Zuo, Jungang Luo, Ni Wang, et al.
Journal of Hydrology (2020) Vol. 585, pp. 124776-124776
Closed Access | Times Cited: 212
Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China
Wei Fang, Shengzhi Huang, Qiang Huang, et al.
Remote Sensing of Environment (2019) Vol. 232, pp. 111290-111290
Closed Access | Times Cited: 194
Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting
Maryam Rahimzad, Alireza Moghaddam Nia, Hesam Zolfonoon, et al.
Water Resources Management (2021) Vol. 35, Iss. 12, pp. 4167-4187
Closed Access | Times Cited: 170
The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction
Rana Muhammad Adnan Ikram, Ahmed A. Ewees, Kulwinder Singh Parmar, et al.
Applied Soft Computing (2022) Vol. 131, pp. 109739-109739
Closed Access | Times Cited: 113
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: 110
Runoff prediction using a multi-scale two-phase processing hybrid model
Xuehua Zhao, Huifang Wang, Qiucen Guo, et al.
Stochastic Environmental Research and Risk Assessment (2025)
Closed Access | Times Cited: 2
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: 139
New insights and best practices for the successful use of Empirical Mode Decomposition, Iterative Filtering and derived algorithms
Angela Stallone, Antonio Cicone, Massimo Materassi
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 130
Examining the applicability of different sampling techniques in the development of decomposition-based streamflow forecasting models
Wei Fang, Shengzhi Huang, Kun Ren, et al.
Journal of Hydrology (2018) Vol. 568, pp. 534-550
Closed Access | Times Cited: 121
Streamflow Prediction Using Deep Learning Neural Network: Case Study of Yangtze River
Darong Liu, Wenchao Jiang, Lin Mu, et al.
IEEE Access (2020) Vol. 8, pp. 90069-90086
Open Access | Times Cited: 121
Forecasting reservoir monthly runoff via ensemble empirical mode decomposition and extreme learning machine optimized by an improved gravitational search algorithm
Wen-jing Niu, Zhong-kai Feng, Ming Zeng, et al.
Applied Soft Computing (2019) Vol. 82, pp. 105589-105589
Closed Access | Times Cited: 115
Comparison of support vector regression and extreme gradient boosting for decomposition-based data-driven 10-day streamflow forecasting
Xiang Yu, Yuhao Wang, Lifeng Wu, et al.
Journal of Hydrology (2019) Vol. 582, pp. 124293-124293
Closed Access | Times Cited: 112
Spatio-temporal characteristics of drought structure across China using an integrated drought index
Shengzhi Huang, Lu Wang, Hao Wang, et al.
Agricultural Water Management (2019) Vol. 218, pp. 182-192
Closed Access | Times Cited: 107
Implementation of evolutionary computing models for reference evapotranspiration modeling: short review, assessment and possible future research directions
Jing Wang, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Shamsuddin Shahid, et al.
Engineering Applications of Computational Fluid Mechanics (2019) Vol. 13, Iss. 1, pp. 811-823
Open Access | Times Cited: 105
Statistical prediction of the severity of compound dry-hot events based on El Niño-Southern Oscillation
Zengchao Hao, Fanghua Hao, Vijay P. Singh, et al.
Journal of Hydrology (2019) Vol. 572, pp. 243-250
Open Access | Times Cited: 100
Non-Linear Input Variable Selection Approach Integrated With Non-Tuned Data Intelligence Model for Streamflow Pattern Simulation
Sinan Jasim Hadi, Sani I. Abba, Saad Sh. Sammen, et al.
IEEE Access (2019) Vol. 7, pp. 141533-141548
Open Access | Times Cited: 98
Copulas-based bivariate socioeconomic drought dynamic risk assessment in a changing environment
Yi Guo, Shengzhi Huang, Qiang Huang, et al.
Journal of Hydrology (2019) Vol. 575, pp. 1052-1064
Closed Access | Times Cited: 96
A Methodological Approach for Predicting COVID-19 Epidemic Using EEMD-ANN Hybrid Model
Najmul Hasan
Internet of Things (2020) Vol. 11, pp. 100228-100228
Open Access | Times Cited: 95
Copulas‐based risk analysis for inter‐seasonal combinations of wet and dry conditions under a changing climate
Wei Fang, Shengzhi Huang, Guohe Huang, et al.
International Journal of Climatology (2018) Vol. 39, Iss. 4, pp. 2005-2021
Closed Access | Times Cited: 91
Automated Parkinson’s disease recognition based on statistical pooling method using acoustic features
Orhan Yaman, Fatih Ertam, Türker Tuncer
Medical Hypotheses (2019) Vol. 135, pp. 109483-109483
Closed Access | Times Cited: 83
Spatio-Temporal Changes and Driving Forces of Vegetation Coverage on the Loess Plateau of Northern Shaanxi
Tong Nie, Guotao Dong, Xiaohui Jiang, et al.
Remote Sensing (2021) Vol. 13, Iss. 4, pp. 613-613
Open Access | Times Cited: 77
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: 66
Historical Water Storage Changes Over China's Loess Plateau
Rui Shao, Baoqing Zhang, Xiaogang He, et al.
Water Resources Research (2021) Vol. 57, Iss. 3
Closed Access | Times Cited: 59
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