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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:

Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms
Ayman Yafouz, Ali Najah Ahmed, Nur’atiah Zaini, et al.
Engineering Applications of Computational Fluid Mechanics (2021) Vol. 15, Iss. 1, pp. 902-933
Open Access | Times Cited: 53

Showing 1-25 of 53 citing articles:

VAR-tree model based spatio-temporal characterization and prediction of O3 concentration in China
Hongbin Dai, Guangqiu Huang, Jingjing Wang, et al.
Ecotoxicology and Environmental Safety (2023) Vol. 257, pp. 114960-114960
Open Access | Times Cited: 53
A systematic literature review of deep learning neural network for time series air quality forecasting
Nur’atiah Zaini, Lee Woen Ean, Ali Najah Ahmed, et al.
Environmental Science and Pollution Research (2021) Vol. 29, Iss. 4, pp. 4958-4990
Closed Access | Times Cited: 77
Novel Genetic Algorithm (GA) based hybrid machine learning-pedotransfer Function (ML-PTF) for prediction of spatial pattern of saturated hydraulic conductivity
Vijay Singh, Kanhu Charan Panda, Atish Sagar, et al.
Engineering Applications of Computational Fluid Mechanics (2022) Vol. 16, Iss. 1, pp. 1082-1099
Open Access | Times Cited: 56
An ensemble approach to improve BPNN model precision for predicting compressive strength of high-performance concrete
Rupesh Kumar Tipu, V. R. Panchal, Kartik S. Pandya
Structures (2022) Vol. 45, pp. 500-508
Closed Access | Times Cited: 54
An ensemble machine learning model for water quality estimation in coastal area based on remote sensing imagery
Xiaotong Zhu, Hongwei Guo, Jinhui Jeanne Huang‬‬‬‬, et al.
Journal of Environmental Management (2022) Vol. 323, pp. 116187-116187
Closed Access | Times Cited: 54
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
Deep learning coupled model based on TCN-LSTM for particulate matter concentration prediction
Ying Ren, Siyuan Wang, Bisheng Xia
Atmospheric Pollution Research (2023) Vol. 14, Iss. 4, pp. 101703-101703
Closed Access | Times Cited: 25
A novel air quality index prediction model based on variational mode decomposition and SARIMA-GA-TCN
Xiaolei Sun, Zhongda Tian
Process Safety and Environmental Protection (2024) Vol. 184, pp. 961-992
Closed Access | Times Cited: 14
Predicting the quality of air with machine learning approaches: Current research priorities and future perspectives
Khalid Mehmood, Yansong Bao, Saif Ullah, et al.
Journal of Cleaner Production (2022) Vol. 379, pp. 134656-134656
Closed Access | Times Cited: 35
A comparison of machine learning models for suspended sediment load classification
Nouar AlDahoul, Ali Najah Ahmed, Mohammed Falah Allawi, et al.
Engineering Applications of Computational Fluid Mechanics (2022) Vol. 16, Iss. 1, pp. 1211-1232
Open Access | Times Cited: 30
Full-coverage spatiotemporal estimation of surface ozone over China based on a high-efficiency deep learning model
Xidong Mu, Sichen Wang, Peng Jiang, et al.
International Journal of Applied Earth Observation and Geoinformation (2023) Vol. 118, pp. 103284-103284
Open Access | Times Cited: 20
Spatial Distribution of Particulate Matter in Iran from Internal Factors to the Role of Western Adjacent Countries from Political Governance to Environmental Governance
Faezeh Borhani, Ali Asghar Pourezzat, Amir Houshang Ehsani
Earth Systems and Environment (2024) Vol. 8, Iss. 1, pp. 135-164
Closed Access | Times Cited: 6
A comparative study of ensemble machine learning models for compressive strength prediction in recycled aggregate concrete and parametric analysis
Pobithra Das, Abul Kashem, Jasim Uddin Rahat, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 4, pp. 3457-3482
Closed Access | Times Cited: 6
Machine learning predictions for carbon monoxide levels in urban environments
Mohammad Abdullah Almubaidin, Nur Shazwani binti Ismail, Sarmad Dashti Latif, et al.
Results in Engineering (2024) Vol. 22, pp. 102114-102114
Open Access | Times Cited: 6
Ozone Concentration Estimation and Meteorological Impact Quantification in the Beijing‐Tianjin‐Hebei Region Based on Machine Learning Models
Zheng Luo, Peilan Lu, Zhe Chen, et al.
Earth and Space Science (2024) Vol. 11, Iss. 2
Open Access | Times Cited: 5
Optimized neural network for daily-scale ozone prediction based on transfer learning
Wei Ma, Zibing Yuan, Alexis K.H. Lau, et al.
The Science of The Total Environment (2022) Vol. 827, pp. 154279-154279
Closed Access | Times Cited: 22
Prediction of atmospheric pollutants in urban environment based on coupled deep learning model and sensitivity analysis
Siyuan Wang, Ying Ren, Bisheng Xia, et al.
Chemosphere (2023) Vol. 331, pp. 138830-138830
Closed Access | Times Cited: 15
Enhancing chloride concentration prediction in marine concrete using conjugate gradient-optimized backpropagation neural network
Rupesh Kumar Tipu, V. R. Panchal, Kartik S. Pandya
Asian Journal of Civil Engineering (2023) Vol. 25, Iss. 1, pp. 637-656
Closed Access | Times Cited: 14
Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction
Ayman Yafouz, Nouar AlDahoul, Ahmed H. Birima, et al.
Alexandria Engineering Journal (2021) Vol. 61, Iss. 6, pp. 4607-4622
Open Access | Times Cited: 27
Spatio‐Temporal Hourly and Daily Ozone Forecasting in China Using a Hybrid Machine Learning Model: Autoencoder and Generative Adversarial Networks
Meiling Cheng, F. Fang, I. M. Navon, et al.
Journal of Advances in Modeling Earth Systems (2022) Vol. 14, Iss. 3
Open Access | Times Cited: 20
Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster
Haitham Abdulmohsin Afan, Ayman Yafouz, Ahmed H. Birima, et al.
Natural Hazards (2022) Vol. 112, Iss. 2, pp. 1527-1545
Closed Access | Times Cited: 19
Assessing uncertainty and heterogeneity in machine learning-based spatiotemporal ozone prediction in Beijing-Tianjin- Hebei region in China
Mei‐Ling Cheng, F. Fang, I. M. Navon, et al.
The Science of The Total Environment (2023) Vol. 881, pp. 163146-163146
Open Access | Times Cited: 10
Forecasting ground-level ozone and fine particulate matter concentrations at Craiova city using a meta-hybrid deep learning model
Y. El Mghouchi, Mihaela Tinca Udriștioiu, Hasan Yıldızhan, et al.
Urban Climate (2024) Vol. 57, pp. 102099-102099
Closed Access | Times Cited: 3
Updated Prediction of Air Quality Based on Kalman-Attention-LSTM Network
Hao Zhou, Tao Wang, Hongchao Zhao, et al.
Sustainability (2022) Vol. 15, Iss. 1, pp. 356-356
Open Access | Times Cited: 16
Atmospheric visibility prediction by using the DBN deep learning model and principal component analysis
Yufeng Wang, Jiamin Du, Zhenyi Yan, et al.
Applied Optics (2022) Vol. 61, Iss. 10, pp. 2657-2657
Closed Access | Times Cited: 15
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