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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 hybrid deep learning technology for PM2.5 air quality forecasting
Zhendong Zhang, Yongkang Zeng, Ke Yan
Environmental Science and Pollution Research (2021) Vol. 28, Iss. 29, pp. 39409-39422
Closed Access | Times Cited: 110

Showing 1-25 of 110 citing articles:

Machine learning algorithms to forecast air quality: a survey
Manuel Méndez, Mercedes G. Merayo, Manuel Núñez
Artificial Intelligence Review (2023) Vol. 56, Iss. 9, pp. 10031-10066
Open Access | Times Cited: 135
Deep-learning architecture for PM2.5 concentration prediction: A review
Shiyun Zhou, Wei Wang, Long Zhu, et al.
Environmental Science and Ecotechnology (2024) Vol. 21, pp. 100400-100400
Open Access | Times Cited: 17
A systematic survey of air quality prediction based on deep learning
Zhen Zhang, Shiqing Zhang, Caimei Chen, et al.
Alexandria Engineering Journal (2024) Vol. 93, pp. 128-141
Open Access | Times Cited: 15
Data Augmentation Strategies for Improved PM2.5 Forecasting Using Transformer Architectures
Phoebe Pan, Anusha Srirenganathan Malarvizhi, Xianjun Hao
Atmosphere (2025) Vol. 16, Iss. 2, pp. 127-127
Open Access | Times Cited: 1
Air quality prediction using CNN+LSTM-based hybrid deep learning architecture
Ayşenur Gılık, Arif Selçuk Öğrencı, Atilla Özmen
Environmental Science and Pollution Research (2021) Vol. 29, Iss. 8, pp. 11920-11938
Closed Access | Times Cited: 83
Deep Learning Enhanced Solar Energy Forecasting with AI-Driven IoT
Hangxia Zhou, Qian Liu, Ke Yan, et al.
Wireless Communications and Mobile Computing (2021) Vol. 2021, pp. 1-11
Open Access | Times Cited: 67
Air quality forecasting with hybrid LSTM and extended stationary wavelet transform
Yongkang Zeng, Jingjing Chen, Ning Jin, et al.
Building and Environment (2022) Vol. 213, pp. 108822-108822
Open Access | Times Cited: 63
Machine learning and deep learning modeling and simulation for predicting PM2.5 concentrations
Jian Peng, Haisheng Han, Yong Yi, et al.
Chemosphere (2022) Vol. 308, pp. 136353-136353
Closed Access | Times Cited: 53
Deep-AIR: A Hybrid CNN-LSTM Framework for Fine-Grained Air Pollution Estimation and Forecast in Metropolitan Cities
Qi Zhang, Yang Han, Victor O. K. Li, et al.
IEEE Access (2022) Vol. 10, pp. 55818-55841
Open Access | Times Cited: 45
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: 42
A new hybrid optimization prediction model for PM2.5 concentration considering other air pollutants and meteorological conditions
Hong Yang, Zehang Liu, Guohui Li
Chemosphere (2022) Vol. 307, pp. 135798-135798
Closed Access | Times Cited: 41
Genetic algorithm-based hyperparameter optimization of deep learning models for PM2.5 time-series prediction
Caner Erden
International Journal of Environmental Science and Technology (2023) Vol. 20, Iss. 3, pp. 2959-2982
Closed Access | Times Cited: 35
Deep learning methods for atmospheric PM2.5 prediction: A comparative study of transformer and CNN-LSTM-attention
Bowen Cui, Minyi Liu, Shanqiang Li, et al.
Atmospheric Pollution Research (2023) Vol. 14, Iss. 9, pp. 101833-101833
Closed Access | Times Cited: 35
Forecasting hourly PM2.5 concentrations based on decomposition-ensemble-reconstruction framework incorporating deep learning algorithms
Peilei Cai, Chengyuan Zhang, Jian Chai
Data Science and Management (2023) Vol. 6, Iss. 1, pp. 46-54
Open Access | Times Cited: 29
A novel spatio-temporal cellular automata model coupling partitioning with CNN-LSTM to urban land change simulation
Ye Zhou, Chen Huang, Tao Wu, et al.
Ecological Modelling (2023) Vol. 482, pp. 110394-110394
Closed Access | Times Cited: 27
Modeling air quality PM2.5 forecasting using deep sparse attention-based transformer networks
Zhenyu Zhang, Shiqing Zhang
International Journal of Environmental Science and Technology (2023) Vol. 20, Iss. 12, pp. 13535-13550
Open Access | Times Cited: 24
An AQI decomposition ensemble model based on SSA-LSTM using improved AMSSA-VMD decomposition reconstruction technique
Kai Wang, Xinyue Fan, Xiaoyi Yang, et al.
Environmental Research (2023) Vol. 232, pp. 116365-116365
Closed Access | Times Cited: 23
Multi-factor PM2.5 concentration optimization prediction model based on decomposition and integration
Hong Yang, Wenqian Wang, Guohui Li
Urban Climate (2024) Vol. 55, pp. 101916-101916
Closed Access | Times Cited: 12
A hybrid optimization prediction model for PM2.5 based on VMD and deep learning
Tao Zeng, XU Li-ping, Yahui Liu, et al.
Atmospheric Pollution Research (2024) Vol. 15, Iss. 7, pp. 102152-102152
Open Access | Times Cited: 10
Balanced Spider Monkey Optimization with Bi-LSTM for Sustainable Air Quality Prediction
C. Aarthi, Varatharaj Jeya Ramya, Przemysław Falkowski‐Gilski, et al.
Sustainability (2023) Vol. 15, Iss. 2, pp. 1637-1637
Open Access | Times Cited: 21
Air quality prediction model based on mRMR–RF feature selection and ISSA–LSTM
Huiyong Wu, Tongtong Yang, Hongkun Li, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 18
A cellular automata model coupled with partitioning CNN-LSTM and PLUS models for urban land change simulation
Chen Huang, Ye Zhou, Tao Wu, et al.
Journal of Environmental Management (2023) Vol. 351, pp. 119828-119828
Closed Access | Times Cited: 17
Study of air contamination in Iraq using remotely sensed Data and GIS
Huda Jamal Jumaah, Mohammed Hashim Ameen, Shakeel Mahmood, et al.
Geocarto International (2023) Vol. 38, Iss. 1
Open Access | Times Cited: 16
Statistical PM2.5 Prediction in an Urban Area Using Vertical Meteorological Factors
Jutapas Saiohai, Surat Bualert, Thunyapat Thongyen, et al.
Atmosphere (2023) Vol. 14, Iss. 3, pp. 589-589
Open Access | Times Cited: 16
A novel hybrid model for hourly PM2.5 prediction considering air pollution factors, meteorological parameters and GNSS-ZTD
Fanming Wu, Pengfei Min, Yan Jin, et al.
Environmental Modelling & Software (2023) Vol. 167, pp. 105780-105780
Open Access | Times Cited: 16
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