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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:

Forecasting PM2.5 levels in Santiago de Chile using deep learning neural networks
Camilo Menares, Patricio Pérez, Santiago Parraguez, et al.
Urban Climate (2021) Vol. 38, pp. 100906-100906
Closed Access | Times Cited: 37

Showing 1-25 of 37 citing articles:

Variational Bayesian Network with Information Interpretability Filtering for Air Quality Forecasting
Xue‐Bo Jin, Zhong-Yao Wang, Wen-Tao Gong, et al.
Mathematics (2023) Vol. 11, Iss. 4, pp. 837-837
Open Access | Times Cited: 46
Environmental optimization of warm mix asphalt (WMA) design with recycled concrete aggregates (RCA) inclusion through artificial intelligence (AI) techniques
Rodrigo Polo-Mendoza, Gilberto Martínez-Arguelles, Rita Peñabaena‐Niebles
Results in Engineering (2023) Vol. 17, pp. 100984-100984
Open Access | Times Cited: 44
Multi-view Stacked CNN-BiLSTM (MvS CNN-BiLSTM) for urban PM2.5 concentration prediction of India’s polluted cities
Subham Kumar, Vipin Kumar
Journal of Cleaner Production (2024) Vol. 444, pp. 141259-141259
Closed Access | Times Cited: 17
Prediction of Monthly PM2.5 Concentration in Liaocheng in China Employing Artificial Neural Network
Zhenfang He, Qingchun Guo, Zhaosheng Wang, et al.
Atmosphere (2022) Vol. 13, Iss. 8, pp. 1221-1221
Open Access | Times Cited: 54
Simulating daily PM2.5 concentrations using wavelet analysis and artificial neural network with remote sensing and surface observation data
Qingchun Guo, Zhenfang He, Zhaosheng Wang
Chemosphere (2023) Vol. 340, pp. 139886-139886
Closed Access | Times Cited: 39
Predicting next hour fine particulate matter (PM2.5) in the Istanbul Metropolitan City using deep learning algorithms with time windowing strategy
Beytullah Eren, İpek Aksangür, Caner Erden
Urban Climate (2023) Vol. 48, pp. 101418-101418
Closed Access | Times Cited: 38
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
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 multi-step ahead point-interval forecasting system for hourly PM2.5 concentrations based on multivariate decomposition and kernel density estimation
Hongtao Li, Yang Yu, Zhipeng Huang, et al.
Expert Systems with Applications (2023) Vol. 226, pp. 120140-120140
Closed Access | Times Cited: 22
ResInformer: Residual Transformer-Based Artificial Time-Series Forecasting Model for PM2.5 Concentration in Three Major Chinese Cities
Mohammed A. A. Al‐qaness, Abdelghani Dahou, Ahmed A. Ewees, et al.
Mathematics (2023) Vol. 11, Iss. 2, pp. 476-476
Open Access | Times Cited: 17
Advancing air quality prediction models in urban India: a deep learning approach integrating DCNN and LSTM architectures for AQI time-series classification
Anurag Barthwal, Amit Kumar Goel
Modeling Earth Systems and Environment (2024) Vol. 10, Iss. 2, pp. 2935-2955
Closed Access | Times Cited: 7
Methods used for handling and quantifying model uncertainty of artificial neural network models for air pollution forecasting
Sheen Mclean Cabaneros, Ben Richard Hughes
Environmental Modelling & Software (2022) Vol. 158, pp. 105529-105529
Open Access | Times Cited: 22
High-Precision Microscale Particulate Matter Prediction in Diverse Environments Using a Long Short-Term Memory Neural Network and Street View Imagery
Xiansheng Liu, Xun Zhang, Rui Wang, et al.
Environmental Science & Technology (2024) Vol. 58, Iss. 8, pp. 3869-3882
Open Access | Times Cited: 5
Machine learning insights into PM2.5 changes during COVID-19 lockdown: LSTM and RF analysis in Mashhad
Seyed Mohammad Mahdi Moezzi, Mitra Mohammadi, Mandana Mohammadi, et al.
Environmental Monitoring and Assessment (2024) Vol. 196, Iss. 5
Closed Access | Times Cited: 5
A temporal domain generalization method for PM concentration prediction based on adversarial training and deep variational information bottleneck
Miaoxuan Shan, Chunlin Ye, Peng Chen, et al.
Atmospheric Pollution Research (2025), pp. 102472-102472
Closed Access
Exploring the significance of temporal, meteorological, and previous states parameters in $$\hbox {PM}_{2.5}$$ concentration predictions: a neural network sensitivity study for Aguascalientes, Mexico
Héctor Antonio Olmos-Guerrero, Pablo T. Rodriguez-Gonzalez, Ramiro Rico-Martı́nez
Modeling Earth Systems and Environment (2025) Vol. 11, Iss. 3
Closed Access
PM2.5 probabilistic forecasting system based on graph generative network with graph U-nets architecture
Yanfei Li, Rui Yang, Zhu Duan, et al.
Journal of Central South University (2025) Vol. 32, Iss. 1, pp. 304-318
Closed Access
PM2.5 concentration forecasting through a novel multi-scale ensemble learning approach considering intercity synergy
Yang Yu, Hongtao Li, Shaolong Sun, et al.
Sustainable Cities and Society (2022) Vol. 85, pp. 104049-104049
Closed Access | Times Cited: 18
Extraction of multi-scale features enhances the deep learning-based daily PM2.5 forecasting in cities
Dong Liang, Pei Hua, Dongwei GUI, et al.
Chemosphere (2022) Vol. 308, pp. 136252-136252
Closed Access | Times Cited: 18
Forecasting daily PM2.5 concentrations in Wuhan with a spatial-autocorrelation-based long short-term memory model
Zhifei Liu, C. Ge, Kang Zheng, et al.
Atmospheric Environment (2024) Vol. 331, pp. 120605-120605
Closed Access | Times Cited: 3
Elevating Hourly PM2.5 Forecasting in Istanbul, Türkiye: Leveraging ERA5 Reanalysis and Genetic Algorithms in a Comparative Machine Learning Model Analysis
Serdar Gündoğdu, Tolga Elbir
Chemosphere (2024) Vol. 364, pp. 143096-143096
Closed Access | Times Cited: 3
Neural networks implementation for the environmental optimisation of the recycled concrete aggregate inclusion in warm mix asphalt
Rodrigo Polo-Mendoza, Gilberto Martínez-Arguelles, Rita Peñabaena‐Niebles, et al.
Road Materials and Pavement Design (2023) Vol. 25, Iss. 5, pp. 941-966
Closed Access | Times Cited: 8
Evaluation of machine learning and deep learning models for daily air quality index prediction in Delhi city, India
Chaitanya Baliram Pande, R. Latha, Megha Satyanarayana
Environmental Monitoring and Assessment (2024) Vol. 196, Iss. 12
Closed Access | Times Cited: 2
Investigation of climate change effects on Iraq dust activity using LSTM
Mehdi Hamidi, Adib Roshani
Atmospheric Pollution Research (2023) Vol. 14, Iss. 10, pp. 101874-101874
Open Access | Times Cited: 6
Space-Time Prediction of PM2.5 Concentrations in Santiago de Chile Using LSTM Networks
Billy Peralta, Tomás Sepúlveda, Orietta Nicolis, et al.
Applied Sciences (2022) Vol. 12, Iss. 22, pp. 11317-11317
Open Access | Times Cited: 10
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