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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:

Revealing Influence of Meteorological Conditions on Air Quality Prediction Using Explainable Deep Learning
Yuting Yang, Gang Mei, Stefano Izzo
IEEE Access (2022) Vol. 10, pp. 50755-50773
Open Access | Times Cited: 24

Showing 24 citing articles:

Forecasting of fine particulate matter based on LSTM and optimization algorithm
Nur’atiah Zaini, Ali Najah Ahmed, Lee Woen Ean, et al.
Journal of Cleaner Production (2023) Vol. 427, pp. 139233-139233
Closed Access | Times Cited: 24
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
Explainable sequence-to-sequence GRU neural network for pollution forecasting
Sara Mirzavand Borujeni, Leila Arras, Vignesh Srinivasan, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 17
Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning
Emrullah Şahin, Naciye Nur Arslan, Durmuş Özdemir
Neural Computing and Applications (2024)
Closed Access | Times Cited: 5
Improving the explainability of CNN-LSTM-based flood prediction with integrating SHAP technique
Hao Huang, Zhaoli Wang, Yaoxing Liao, et al.
Ecological Informatics (2024) Vol. 84, pp. 102904-102904
Open Access | Times Cited: 4
Material- based approaches for efficient forecasting and mitigation of air pollution using advanced neural network models
Jamuna Mylan, Gandhimathi Arunachalam
Matéria (Rio de Janeiro) (2025) Vol. 30
Open Access
Interpretable Machine Learning Approaches for Forecasting and Predicting Air Pollution: A Systematic Review
Anass Houdou, Imad El Badisy, Kenza Khomsi, et al.
Aerosol and Air Quality Research (2023) Vol. 24, Iss. 1, pp. 230151-230151
Open Access | Times Cited: 12
A Prediction Hybrid Framework for Air Quality Integrated with W-BiLSTM(PSO)-GRU and XGBoost Methods
Wenbing Chang, Xu Chen, He Zhao, et al.
Sustainability (2023) Vol. 15, Iss. 22, pp. 16064-16064
Open Access | Times Cited: 10
Forecasting PM2.5 Concentration Using Gradient-Boosted Regression Tree with CNN Learning Model
A. Usha Ruby, J. George Chellin Chandran, Prasannavenkatesan Theerthagiri, et al.
Optical Memory and Neural Networks (2024) Vol. 33, Iss. 1, pp. 86-96
Closed Access | Times Cited: 3
An Improved Deep Learning Approach Considering Spatiotemporal Heterogeneity for PM2.5 Prediction: A Case Study of Xinjiang, China
Yajing Wu, Zhangyan Xu, Liping Xu, et al.
Atmosphere (2024) Vol. 15, Iss. 4, pp. 460-460
Open Access | Times Cited: 3
Contribution of ecological restoration projects to long-term changes in PM2.5
Yulu Yang, Mingchang Shi, Baojian Liu, et al.
Ecological Indicators (2024) Vol. 159, pp. 111630-111630
Open Access | Times Cited: 2
Design and Enhancement of a Fog-Enabled Air Quality Monitoring and Prediction System: An Optimized Lightweight Deep Learning Model for a Smart Fog Environmental Gateway
P. Divya Bharathi, V. Anantha Narayanan, P. Bagavathi Sivakumar
Sensors (2024) Vol. 24, Iss. 15, pp. 5069-5069
Open Access | Times Cited: 2
Implementing heuristic-based multiscale depth-wise separable adaptive temporal convolutional network for ambient air quality prediction using real time data
Raj Anand Sundaramoorthy, A. Dennis Ananth, Koteeswaran Seerangan, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
TinyML Models for a Low-Cost Air Quality Monitoring Device
I Nyoman Kusuma Wardana, Suhaib A. Fahmy, Julian W. Gardner
IEEE Sensors Letters (2023) Vol. 7, Iss. 11, pp. 1-4
Open Access | Times Cited: 6
Explainable Artificial Intelligence (XAI) for Air Quality Assessment
Sayan Chakraborty, Bitan Misra, Nilanjan Dey
Frontiers in artificial intelligence and applications (2024)
Open Access | Times Cited: 1
A study on air quality prediction with multiple features based on GCN-LSTM
Yongming Jin, Ge Ren, Yuxin Hu, et al.
Journal of Physics Conference Series (2024) Vol. 2816, Iss. 1, pp. 012074-012074
Open Access | Times Cited: 1
Investigating the effect of estimating urban air pollution considering transportation infrastructure layouts
Xiaojian Hu, Xiatong Hao, Ke Zhang, et al.
Transportation Research Part D Transport and Environment (2024) Vol. 139, pp. 104569-104569
Closed Access | Times Cited: 1
Exploring influence of groundwater and lithology on data-driven stability prediction of soil slopes using explainable machine learning: a case study
Wenxue Gao, Mingdong Zang, Gang Mei
Bulletin of Engineering Geology and the Environment (2023) Vol. 83, Iss. 1
Closed Access | Times Cited: 4
Novel Regression and Least Square Support Vector Machine Learning Technique for Air Pollution Forecasting
M Dhanalakshmi, V Radha
International Journal of Engineering Trends and Technology (2023) Vol. 71, Iss. 4, pp. 147-158
Open Access | Times Cited: 2
Deep Learning Techniques for Air Quality Prediction: A Focus on PM2.5 and Periodicity
Lakshmi Shankar, A. Krishnamoorthy
MIGRATION LETTERS (2023) Vol. 20, Iss. S13, pp. 468-484
Open Access | Times Cited: 2
Future Air Quality Prediction Using Long Short-Term Memory Based on Hyper Heuristic Multi-Chain Model
Kalyan Chatterjee, Samla Suraj Kumar, Ramagiri Praveen Kumar, et al.
IEEE Access (2024) Vol. 12, pp. 123678-123693
Open Access
An adaptive serial cascaded autoencoder and LSTM with multivariate regression for ambient air quality prediction with improved flow direction algorithm
M. Lakshmipathy, Shanthi Prasad Mysore Jeevandharakumar, G. N. Kodandaramaiah
Energy Sources Part A Recovery Utilization and Environmental Effects (2023) Vol. 45, Iss. 4, pp. 10304-10329
Closed Access | Times Cited: 1
H2MaM: Future Air Quality Prediction using LSTM-based Hyper Heuristic Multi-Chain Model
Sweta Dey, Kalyan Chatterjee, Anjan Bandyopadhyay, et al.
(2023)
Open Access
H2MaM: Future Air Quality Prediction using LSTM-based Hyper Heuristic Multi-Chain Model
Sweta Dey, Kalyan Chatterjee, Anjan Bandyopadhyay, et al.
(2023)
Open Access
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