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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 review of artificial neural network models for ambient air pollution prediction
Sheen Mclean Cabaneros, John Kaiser Calautit, Ben Richard Hughes
Environmental Modelling & Software (2019) Vol. 119, pp. 285-304
Open Access | Times Cited: 386

Showing 1-25 of 386 citing articles:

A Review of the Artificial Neural Network Models for Water Quality Prediction
Yingyi Chen, Lihua Song, Yeqi Liu, et al.
Applied Sciences (2020) Vol. 10, Iss. 17, pp. 5776-5776
Open Access | Times Cited: 314
A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance
Adil Masood, Kafeel Ahmad
Journal of Cleaner Production (2021) Vol. 322, pp. 129072-129072
Closed Access | Times Cited: 151
Air Quality Index and Air Pollutant Concentration Prediction Based on Machine Learning Algorithms
Huixiang Liu, Qing Li, Dongbing Yu, et al.
Applied Sciences (2019) Vol. 9, Iss. 19, pp. 4069-4069
Open Access | Times Cited: 149
A deep learning approach for prediction of air quality index in a metropolitan city
R. Janarthanan, Pachaivannan Partheeban, K. Somasundaram, et al.
Sustainable Cities and Society (2021) Vol. 67, pp. 102720-102720
Closed Access | Times Cited: 148
Deep learning for air pollutant concentration prediction: A review
Bo Zhang, Yi Rong, Ruihan Yong, et al.
Atmospheric Environment (2022) Vol. 290, pp. 119347-119347
Closed Access | Times Cited: 125
Intelligent modeling strategies for forecasting air quality time series: A review
Hui Liu, Guangxi Yan, Zhu Duan, et al.
Applied Soft Computing (2021) Vol. 102, pp. 106957-106957
Closed Access | Times Cited: 122
Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China
Qingchun Guo, Zhenfang He, Zhaosheng Wang
Toxics (2023) Vol. 11, Iss. 1, pp. 51-51
Open Access | Times Cited: 51
Exploding the myths: An introduction to artificial neural networks for prediction and forecasting
Holger R. Maier, Stefano Galelli, Saman Razavi, et al.
Environmental Modelling & Software (2023) Vol. 167, pp. 105776-105776
Open Access | Times Cited: 50
A hybrid deep learning model for regional O3 and NO2 concentrations prediction based on spatiotemporal dependencies in air quality monitoring network
Cui-Lin Wu, Hong-di He, Rui-feng Song, et al.
Environmental Pollution (2023) Vol. 320, pp. 121075-121075
Closed Access | Times Cited: 45
Towards objective human performance measurement for maritime safety: A new psychophysiological data-driven machine learning method
Shiqi Fan, Zaili Yang
Reliability Engineering & System Safety (2023) Vol. 233, pp. 109103-109103
Open Access | Times Cited: 42
A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023
Xuying Ma, Bin Zou, Jun Deng, et al.
Environment International (2024) Vol. 183, pp. 108430-108430
Open Access | Times Cited: 37
Carbon emission prediction models: A review
Yukai Jin, Ayyoob Sharifi, Zhisheng Li, et al.
The Science of The Total Environment (2024) Vol. 927, pp. 172319-172319
Closed Access | Times Cited: 31
Other’s shoes also fit well: AI technologies contribute to China’s blue skies as well as carbon reduction
Zhongzhu Chu, Pengyu Chen, Zihan Zhang, et al.
Journal of Environmental Management (2024) Vol. 353, pp. 120171-120171
Closed Access | Times Cited: 19
Artificial neural network an innovative approach in air pollutant prediction for environmental applications: A review
Vibha Yadav, Amit Kumar Yadav, Vedant Singh, et al.
Results in Engineering (2024) Vol. 22, pp. 102305-102305
Open Access | Times Cited: 17
Practical artificial neural network tool for predicting the axial compression capacity of circular concrete-filled steel tube columns with ultra-high-strength concrete
Viet‐Linh Tran, Duc‐Kien Thai, Duy-Duan Nguyen
Thin-Walled Structures (2020) Vol. 151, pp. 106720-106720
Closed Access | Times Cited: 116
Deep Learning for Prediction of the Air Quality Response to Emission Changes
Jia Xing, Shuxin Zheng, Dian Ding, et al.
Environmental Science & Technology (2020) Vol. 54, Iss. 14, pp. 8589-8600
Open Access | Times Cited: 99
Air pollution and its health impacts in Malaysia: a review
Raja Sher Afgun Usmani, Anum Saeed, Akibu Mahmoud Abdullahi, et al.
Air Quality Atmosphere & Health (2020) Vol. 13, Iss. 9, pp. 1093-1118
Closed Access | Times Cited: 97
Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges
Lü Liang
Environmental Research (2021) Vol. 197, pp. 111163-111163
Closed Access | Times Cited: 93
Trace Metals in the Environment - New Approaches and Recent Advances
Mario Alfonso Murillo-Tovar, Hugo Saldarriaga-Noreña, Agnieszka Saeid, et al.
IntechOpen eBooks (2019)
Open Access | Times Cited: 90
Ensemble method based on Artificial Neural Networks to estimate air pollution health risks
Lilian N. Araujo, Jônatas Trabuco Belotti, Thiago Antonini Alves, et al.
Environmental Modelling & Software (2019) Vol. 123, pp. 104567-104567
Closed Access | Times Cited: 79
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
Disparities in particulate matter (PM<sub>10</sub>) origins and oxidative potential at a city scale (Grenoble, France) – Part 2: Sources of PM<sub>10</sub> oxidative potential using multiple linear regression analysis and the predictive applicability of multilayer perceptron neural network analysis
Lucille Joanna S. Borlaza, Samuël Weber, Jean‐Luc Jaffrezo, et al.
Atmospheric chemistry and physics (2021) Vol. 21, Iss. 12, pp. 9719-9739
Open Access | Times Cited: 70
Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air
Seyedeh Reyhaneh Shams, Ali Jahani, Saba Kalantary, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 61
Artificial intelligence-based microfluidic platforms for the sensitive detection of environmental pollutants: Recent advances and prospects
Niki Pouyanfar, Samaneh Zare Harofte, Maha Soltani, et al.
Trends in Environmental Analytical Chemistry (2022) Vol. 34, pp. e00160-e00160
Closed Access | Times Cited: 59
Spatiotemporal causal convolutional network for forecasting hourly PM2.5 concentrations in Beijing, China
Lei Zhang, Jiaming Na, Jie Zhu, et al.
Computers & Geosciences (2021) Vol. 155, pp. 104869-104869
Closed Access | Times Cited: 55
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