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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:

Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction
Sujan Ghimire, Ravinesh C. Deo, Nawin Raj, et al.
Energies (2019) Vol. 12, Iss. 12, pp. 2407-2407
Open Access | Times Cited: 84

Showing 1-25 of 84 citing articles:

Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms
Sujan Ghimire, Ravinesh C. Deo, Nawin Raj, et al.
Applied Energy (2019) Vol. 253, pp. 113541-113541
Closed Access | Times Cited: 332
Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations
Haixiang Zang, Ling Liu, Li Sun, et al.
Renewable Energy (2020) Vol. 160, pp. 26-41
Closed Access | Times Cited: 309
A review on global solar radiation prediction with machine learning models in a comprehensive perspective
Yong Zhou, Yanfeng Liu, Dengjia Wang, et al.
Energy Conversion and Management (2021) Vol. 235, pp. 113960-113960
Closed Access | Times Cited: 170
Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models
Junliang Fan, Jing Zheng, Lifeng Wu, et al.
Agricultural Water Management (2020) Vol. 245, pp. 106547-106547
Closed Access | Times Cited: 164
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: 109
Stacked LSTM Sequence-to-Sequence Autoencoder with Feature Selection for Daily Solar Radiation Prediction: A Review and New Modeling Results
Sujan Ghimire, Ravinesh C. Deo, Hua Wang, et al.
Energies (2022) Vol. 15, Iss. 3, pp. 1061-1061
Open Access | Times Cited: 75
Machine learning models to quantify and map daily global solar radiation and photovoltaic power
Yu Feng, Weiping Hao, Haoru Li, et al.
Renewable and Sustainable Energy Reviews (2019) Vol. 118, pp. 109393-109393
Closed Access | Times Cited: 134
Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia
Ellysia Jumin, Faridah Basaruddin, Yuzainee Bte. Md Yusoff, et al.
Environmental Science and Pollution Research (2021) Vol. 28, Iss. 21, pp. 26571-26583
Closed Access | Times Cited: 94
A hybrid air quality early-warning framework: An hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms
Ekta Sharma, Ravinesh C. Deo, Ramendra Prasad, et al.
The Science of The Total Environment (2019) Vol. 709, pp. 135934-135934
Closed Access | Times Cited: 91
Efficient daily solar radiation prediction with deep learning 4-phase convolutional neural network, dual stage stacked regression and support vector machine CNN-REGST hybrid model
Sujan Ghimire, Thong Nguyen‐Huy, Ravinesh C. Deo, et al.
Sustainable materials and technologies (2022) Vol. 32, pp. e00429-e00429
Closed Access | Times Cited: 65
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: 64
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: 64
Hybrid deep CNN-SVR algorithm for solar radiation prediction problems in Queensland, Australia
Sujan Ghimire, Binayak Bhandari, David Casillas-Pérez, et al.
Engineering Applications of Artificial Intelligence (2022) Vol. 112, pp. 104860-104860
Open Access | Times Cited: 63
Deep learning CNN-LSTM-MLP hybrid fusion model for feature optimizations and daily solar radiation prediction
Sujan Ghimire, Ravinesh C. Deo, David Casillas-Pérez, et al.
Measurement (2022) Vol. 202, pp. 111759-111759
Open Access | Times Cited: 62
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: 47
Hybrid Convolutional Neural Network-Multilayer Perceptron Model for Solar Radiation Prediction
Sujan Ghimire, Thong Nguyen‐Huy, Ramendra Prasad, et al.
Cognitive Computation (2022) Vol. 15, Iss. 2, pp. 645-671
Closed Access | Times Cited: 44
Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Deep Residual model for short-term multi-step solar radiation prediction
Sujan Ghimire, Ravinesh C. Deo, David Casillas-Pérez, et al.
Renewable Energy (2022) Vol. 190, pp. 408-424
Closed Access | Times Cited: 43
Using Machine Learning Models to Predict Hydroponically Grown Lettuce Yield
Ali Mokhtar, Wessam El-Ssawy, Hongming He, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 42
Novel hybrid deep learning model for satellite based PM10 forecasting in the most polluted Australian hotspots
Ekta Sharma, Ravinesh C. Deo, Jeffrey Soar, et al.
Atmospheric Environment (2022) Vol. 279, pp. 119111-119111
Open Access | Times Cited: 41
Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model
Yunbo Lu, Lunche Wang, Canming Zhu, et al.
Renewable and Sustainable Energy Reviews (2022) Vol. 173, pp. 113105-113105
Closed Access | Times Cited: 38
A novel combined multi-task learning and Gaussian process regression model for the prediction of multi-timescale and multi-component of solar radiation
Yong Zhou, Yanfeng Liu, Dengjia Wang, et al.
Journal of Cleaner Production (2020) Vol. 284, pp. 124710-124710
Closed Access | Times Cited: 68
Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia
Mohanad S. AL‐Musaylh, Ravinesh C. Deo, Jan Adamowski, et al.
Renewable and Sustainable Energy Reviews (2019) Vol. 113, pp. 109293-109293
Closed Access | Times Cited: 60
Deep Air Quality Forecasts: Suspended Particulate Matter Modeling With Convolutional Neural and Long Short-Term Memory Networks
Ekta Sharma, Ravinesh C. Deo, Ramendra Prasad, et al.
IEEE Access (2020) Vol. 8, pp. 209503-209516
Open Access | Times Cited: 49
Boosting algorithms in energy research: a systematic review
Hristos Tyralis, Georgia Papacharalampous
Neural Computing and Applications (2021) Vol. 33, Iss. 21, pp. 14101-14117
Open Access | Times Cited: 49
Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm
A. Aljanad, Nadia M. L. Tan, Vassilios G. Agelidis, et al.
Energies (2021) Vol. 14, Iss. 4, pp. 1213-1213
Open Access | Times Cited: 45
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