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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:

Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques
Jun Ma, Jack C.P. Cheng, Changqing Lin, et al.
Atmospheric Environment (2019) Vol. 214, pp. 116885-116885
Closed Access | Times Cited: 212

Showing 1-25 of 212 citing articles:

Machine Learning: New Ideas and Tools in Environmental Science and Engineering
Shifa Zhong, Kai Zhang, Majid Bagheri, et al.
Environmental Science & Technology (2021)
Closed Access | Times Cited: 643
AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
Yassine Himeur, Mariam Elnour, Fodil Fadli, et al.
Artificial Intelligence Review (2022) Vol. 56, Iss. 6, pp. 4929-5021
Open Access | Times Cited: 257
Air quality prediction at new stations using spatially transferred bi-directional long short-term memory network
Jun Ma, Zheng Li, Jack C.P. Cheng, et al.
The Science of The Total Environment (2019) Vol. 705, pp. 135771-135771
Closed Access | Times Cited: 152
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 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
A novel wind speed prediction strategy based on Bi-LSTM, MOOFADA and transfer learning for centralized control centers
Tao Liang, Qing Zhao, Qingzhao Lv, et al.
Energy (2021) Vol. 230, pp. 120904-120904
Closed Access | Times Cited: 102
Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions
Tao Hai, Sani I. Abba, Ahmed M. Al‐Areeq, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 129, pp. 107559-107559
Closed Access | Times Cited: 58
A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions
Peng Yan, Ahmed Abdulkadir, Paul-Philipp Luley, et al.
IEEE Access (2024) Vol. 12, pp. 3768-3789
Open Access | Times Cited: 39
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
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 bi-directional missing data imputation scheme based on LSTM and transfer learning for building energy data
Jun Ma, Jack C.P. Cheng, Feifeng Jiang, et al.
Energy and Buildings (2020) Vol. 216, pp. 109941-109941
Closed Access | Times Cited: 132
A hybrid deep transfer learning strategy for short term cross-building energy prediction
Xi Fang, Guangcai Gong, Guannan Li, et al.
Energy (2020) Vol. 215, pp. 119208-119208
Closed Access | Times Cited: 128
Spatial prediction of PM10 concentration using machine learning algorithms in Ankara, Turkey
Aslı Bozdağ, Yeşim DOKUZ, Öznur Begüm Gökçek
Environmental Pollution (2020) Vol. 263, pp. 114635-114635
Closed Access | Times Cited: 106
Analyzing driving factors of land values in urban scale based on big data and non-linear machine learning techniques
Jun Ma, Jack C.P. Cheng, Feifeng Jiang, et al.
Land Use Policy (2020) Vol. 94, pp. 104537-104537
Closed Access | Times Cited: 99
A Lag-FLSTM deep learning network based on Bayesian Optimization for multi-sequential-variant PM2.5 prediction
Jun Ma, Yuexiong Ding, Jack C.P. Cheng, et al.
Sustainable Cities and Society (2020) Vol. 60, pp. 102237-102237
Closed Access | Times Cited: 97
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
RETRACTED ARTICLE: Development of air quality monitoring (AQM) models using different machine learning approaches
C. Amuthadevi, D. S. Vijayan, V. Ramachandran
Journal of Ambient Intelligence and Humanized Computing (2021) Vol. 13, Iss. S1, pp. 33-33
Closed Access | Times Cited: 83
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
Soft detection of 5-day BOD with sparse matrix in city harbor water using deep learning techniques
Jun Ma, Yuexiong Ding, Jack C.P. Cheng, et al.
Water Research (2019) Vol. 170, pp. 115350-115350
Closed Access | Times Cited: 81
A new dam structural response estimation paradigm powered by deep learning and transfer learning techniques
Yangtao Li, Tengfei Bao, Zhixin Gao, et al.
Structural Health Monitoring (2021) Vol. 21, Iss. 3, pp. 770-787
Closed Access | Times Cited: 79
A review of the inter-correlation of climate change, air pollution and urban sustainability using novel machine learning algorithms and spatial information science
Abdul‐Lateef Balogun, Abdulwaheed Tella, Lavania Baloo, et al.
Urban Climate (2021) Vol. 40, pp. 100989-100989
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: 79
Combining transfer learning and hyperspectral reflectance analysis to assess leaf nitrogen concentration across different plant species datasets
Liang Wan, Weijun Zhou, Yong He, et al.
Remote Sensing of Environment (2021) Vol. 269, pp. 112826-112826
Closed Access | Times Cited: 77
Spatio-temporal modeling of PM2.5 risk mapping using three machine learning algorithms
Seyedeh Zeinab Shogrkhodaei, Seyed Vahid Razavi-Termeh, Amanollah Fathnia
Environmental Pollution (2021) Vol. 289, pp. 117859-117859
Closed Access | Times Cited: 68
Application of deep learning in facility management and maintenance for heating, ventilation, and air conditioning
Mirza Rayana Sanzana, Tomás Maul, Jing Ying Wong, et al.
Automation in Construction (2022) Vol. 141, pp. 104445-104445
Closed Access | Times Cited: 58
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