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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:

Weekly soil moisture forecasting with multivariate sequential, ensemble empirical mode decomposition and Boruta-random forest hybridizer algorithm approach
Ramendra Prasad, Ravinesh C. Deo, Yan Li, et al.
CATENA (2019) Vol. 177, pp. 149-166
Closed Access | Times Cited: 136

Showing 1-25 of 136 citing articles:

Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization
Rana Muhammad Adnan, Reham R. Mostafa, Özgür Kişi, et al.
Knowledge-Based Systems (2021) Vol. 230, pp. 107379-107379
Closed Access | Times Cited: 165
Coupling a hybrid CNN-LSTM deep learning model with a Boundary Corrected Maximal Overlap Discrete Wavelet Transform for multiscale Lake water level forecasting
Rahim Barzegar, Mohammad Taghi Aalami, Jan Adamowski
Journal of Hydrology (2021) Vol. 598, pp. 126196-126196
Closed Access | Times Cited: 154
Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity
A. A. Masrur Ahmed, Ravinesh C. Deo, Qi Feng, et al.
Journal of Hydrology (2021) Vol. 599, pp. 126350-126350
Closed Access | Times Cited: 112
Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions
Halit Apaydın, Mohammad Taghi Sattari, Kambiz Falsafian, et al.
Journal of Hydrology (2021) Vol. 600, pp. 126506-126506
Closed Access | Times Cited: 112
Research on Water Resource Modeling Based on Machine Learning Technologies
Liu Ze, Jingzhao Zhou, Xiaoyang Yang, et al.
Water (2024) Vol. 16, Iss. 3, pp. 472-472
Open Access | Times Cited: 19
A comprehensive study of deep learning for soil moisture prediction
Yanling Wang, Liangsheng Shi, Yaan Hu, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 4, pp. 917-943
Open Access | Times Cited: 15
Soil moisture simulation using hybrid artificial intelligent model: Hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm
Saman Maroufpoor, Eisa Maroufpoor, Omid Bozorg‐Haddad, et al.
Journal of Hydrology (2019) Vol. 575, pp. 544-556
Closed Access | Times Cited: 122
Prediction of seven-day compressive strength of field concrete
Xueqing Zhang, Muhammad Zeshan Akber, Wei Zheng
Construction and Building Materials (2021) Vol. 305, pp. 124604-124604
Closed Access | Times Cited: 66
LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios
A. A. Masrur Ahmed, Ravinesh C. Deo, Afshin Ghahramani, et al.
Stochastic Environmental Research and Risk Assessment (2021) Vol. 35, Iss. 9, pp. 1851-1881
Closed Access | Times Cited: 65
Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data
A. A. Masrur Ahmed, Ravinesh C. Deo, Nawin Raj, et al.
Remote Sensing (2021) Vol. 13, Iss. 4, pp. 554-554
Open Access | Times Cited: 65
UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat
Zongpeng Li, Zhen Chen, Qian Cheng, et al.
Agronomy (2022) Vol. 12, Iss. 1, pp. 202-202
Open Access | Times Cited: 65
A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations
Anna E. Sikorska‐Senoner, John Quilty
Environmental Modelling & Software (2021) Vol. 143, pp. 105094-105094
Open Access | Times Cited: 63
New double decomposition deep learning methods for river water level forecasting
A. A. Masrur Ahmed, Ravinesh C. Deo, Afshin Ghahramani, et al.
The Science of The Total Environment (2022) Vol. 831, pp. 154722-154722
Closed Access | Times Cited: 48
Short-term prediction of reference crop evapotranspiration based on machine learning with different decomposition methods in arid areas of China
Yingjie Lu, Tao Li, Hui Hu, et al.
Agricultural Water Management (2023) Vol. 279, pp. 108175-108175
Open Access | Times Cited: 33
Nitrate prediction in groundwater of data scarce regions: The futuristic fresh-water management outlook
Jürgen Mahlknecht, Juan Antonio Torres-Martínez, Manish Kumar, et al.
The Science of The Total Environment (2023) Vol. 905, pp. 166863-166863
Open Access | Times Cited: 22
Forecasting tourism demand with search engine data: A hybrid CNN-BiLSTM model based on Boruta feature selection
J. Chen, Zhihao Ying, Chonghui Zhang, et al.
Information Processing & Management (2024) Vol. 61, Iss. 3, pp. 103699-103699
Closed Access | Times Cited: 13
An explainable multiscale LSTM model with wavelet transform and layer-wise relevance propagation for daily streamflow forecasting
Lizhi Tao, Zhichao Cui, Yufeng He, et al.
The Science of The Total Environment (2024) Vol. 929, pp. 172465-172465
Closed Access | Times Cited: 12
Quantitative improvement of streamflow forecasting accuracy in the Atlantic zones of Canada based on hydro-meteorological signals: A multi-level advanced intelligent expert framework
Mozhdeh Jamei, Mehdi Jamei, Mumtaz Ali, et al.
Ecological Informatics (2024) Vol. 80, pp. 102455-102455
Open Access | Times Cited: 10
Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm
櫻井 克年, Xiangxiang Su, Yue Hu, et al.
Frontiers in Plant Science (2024) Vol. 15
Open Access | Times Cited: 8
Hybrid machine learning system based on multivariate data decomposition and feature selection for improved multitemporal evapotranspiration forecasting
Jinwook Lee, Sayed M. Bateni, Changhyun Jun, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 135, pp. 108744-108744
Closed Access | Times Cited: 8
Comparison of the use of a physical-based model with data assimilation and machine learning methods for simulating soil water dynamics
Peijun Li, Yuanyuan Zha, Liangsheng Shi, et al.
Journal of Hydrology (2020) Vol. 584, pp. 124692-124692
Closed Access | Times Cited: 69
Prediction of Suspended Sediment Load Using Data-Driven Models
Rana Muhammad Adnan Ikram, Zhongmin Liang, Ahmed El‐Shafie, et al.
Water (2019) Vol. 11, Iss. 10, pp. 2060-2060
Open Access | Times Cited: 66
A double decomposition-based modelling approach to forecast weekly solar radiation
Ramendra Prasad, Mumtaz Ali, Yong Xiang, et al.
Renewable Energy (2020) Vol. 152, pp. 9-22
Closed Access | Times Cited: 63
An EEMD-BiLSTM Algorithm Integrated with Boruta Random Forest Optimiser for Significant Wave Height Forecasting along Coastal Areas of Queensland, Australia
Nawin Raj, Jason Brown
Remote Sensing (2021) Vol. 13, Iss. 8, pp. 1456-1456
Open Access | Times Cited: 49
Variational mode decomposition based random forest model for solar radiation forecasting: New emerging machine learning technology
Mumtaz Ali, Ramendra Prasad, Yong Xiang, et al.
Energy Reports (2021) Vol. 7, pp. 6700-6717
Open Access | Times Cited: 49
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