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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:

Seamless integration of convolutional and back-propagation neural networks for regional multi-step-ahead PM2.5 forecasting
Pu-Yun Kow, Yi-Shin Wang, Yanlai Zhou, et al.
Journal of Cleaner Production (2020) Vol. 261, pp. 121285-121285
Open Access | Times Cited: 81

Showing 1-25 of 81 citing articles:

Artificial neural network model to predict the compressive strength of eco-friendly geopolymer concrete incorporating silica fume and natural zeolite
Amir Ali Shahmansouri, Maziar Yazdani, Saeed Ghanbari, et al.
Journal of Cleaner Production (2020) Vol. 279, pp. 123697-123697
Closed Access | Times Cited: 274
NOx emission prediction using a lightweight convolutional neural network for cleaner production in a down-fired boiler
Zhi Wang, Xianyong Peng, Shengxian Cao, et al.
Journal of Cleaner Production (2023) Vol. 389, pp. 136060-136060
Closed Access | Times Cited: 57
Predicting high-resolution air quality using machine learning: Integration of large eddy simulation and urban morphology data
Shibao Wang, Jeremy McGibbon, Yanxu Zhang
Environmental Pollution (2024) Vol. 344, pp. 123371-123371
Closed Access | Times Cited: 16
Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniques
Fi‐John Chang, Li‐Chiu Chang, Che-Chia Kang, et al.
The Science of The Total Environment (2020) Vol. 736, pp. 139656-139656
Closed Access | Times Cited: 83
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
Predictions and mitigation strategies of PM2.5 concentration in the Yangtze River Delta of China based on a novel nonlinear seasonal grey model
Weijie Zhou, Xiaoli Wu, Song Ding, et al.
Environmental Pollution (2021) Vol. 276, pp. 116614-116614
Closed Access | Times Cited: 67
Economy and carbon emissions optimization of different countries or areas in the world using an improved Attention mechanism based long short term memory neural network
Xiaoyong Lin, X.P. Zhu, Mingfei Feng, et al.
The Science of The Total Environment (2021) Vol. 792, pp. 148444-148444
Closed Access | Times Cited: 56
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
Short-Term Prediction of PM2.5 Using LSTM Deep Learning Methods
Endah Kristiani, Hao Lin, Jwu‐Rong Lin, et al.
Sustainability (2022) Vol. 14, Iss. 4, pp. 2068-2068
Open Access | Times Cited: 48
A Hybrid Spatiotemporal Deep Model Based on CNN and LSTM for Air Pollution Prediction
Stefan Tsokov, Milena Lazarova, Adelina Aleksieva-Petrova
Sustainability (2022) Vol. 14, Iss. 9, pp. 5104-5104
Open Access | Times Cited: 42
Deep neural networks for spatiotemporal PM2.5 forecasts based on atmospheric chemical transport model output and monitoring data
Pu-Yun Kow, Li‐Chiu Chang, Chuan‐Yao Lin, et al.
Environmental Pollution (2022) Vol. 306, pp. 119348-119348
Closed Access | Times Cited: 42
A new ensemble spatio-temporal PM2.5 prediction method based on graph attention recursive networks and reinforcement learning
Jing Tan, Hui Liu, Yanfei Li, et al.
Chaos Solitons & Fractals (2022) Vol. 162, pp. 112405-112405
Closed Access | Times Cited: 42
Real-time image-based air quality estimation by deep learning neural networks
Pu-Yun Kow, I-Wen Hsia, Li‐Chiu Chang, et al.
Journal of Environmental Management (2022) Vol. 307, pp. 114560-114560
Closed Access | Times Cited: 39
Forecasting air pollutant concentration using a novel spatiotemporal deep learning model based on clustering, feature selection and empirical wavelet transform
Jusong Kim, Xiaoli Wang, Chollyong Kang, et al.
The Science of The Total Environment (2021) Vol. 801, pp. 149654-149654
Closed Access | Times Cited: 44
Deep learning with data preprocessing methods for water quality prediction in ultrafiltration
Jaegyu Shim, Seokmin Hong, Jiye Lee, et al.
Journal of Cleaner Production (2023) Vol. 428, pp. 139217-139217
Closed Access | Times Cited: 19
A deep spatio-temporal learning network for continuous citywide air quality forecast based on dense monitoring data
Rong Guo, Qiang Zhang, Xin Yu, et al.
Journal of Cleaner Production (2023) Vol. 414, pp. 137568-137568
Closed Access | Times Cited: 16
A spatial multi-resolution multi-objective data-driven ensemble model for multi-step air quality index forecasting based on real-time decomposition
Hui Liu, Rui Yang
Computers in Industry (2021) Vol. 125, pp. 103387-103387
Closed Access | Times Cited: 36
A deep learning approach to model daily particular matter of Ankara: key features and forecasting
Yıldırım Akbal, Kamil Demirberk Ünlü
International Journal of Environmental Science and Technology (2021) Vol. 19, Iss. 7, pp. 5911-5927
Closed Access | Times Cited: 33
An Improved MODIS NIR PWV Retrieval Algorithm Based on an Artificial Neural Network Considering the Land-Cover Types
Xiongwei Ma, Yibin Yao, Bao Zhang, et al.
IEEE Transactions on Geoscience and Remote Sensing (2022) Vol. 60, pp. 1-12
Closed Access | Times Cited: 24
Deep non-crossing probabilistic wind speed forecasting with multi-scale features
Runmin Zou, Mengmeng Song, Yun Wang, et al.
Energy Conversion and Management (2022) Vol. 257, pp. 115433-115433
Closed Access | Times Cited: 23
The Use of TensorFlow in Analyzing Air Quality Artificial Intelligence Predictions PM2.5
Untung Rahardja, Qurotul Aini, Po Abas Sunarya, et al.
Aptisi Transactions On Technopreneurship (ATT) (2022) Vol. 4, Iss. 3, pp. 313-324
Open Access | Times Cited: 23
Spatiotemporal informer: A new approach based on spatiotemporal embedding and attention for air quality forecasting
Yang Feng, Ju-Song Kim, Jin‐Won Yu, et al.
Environmental Pollution (2023) Vol. 336, pp. 122402-122402
Closed Access | Times Cited: 13
Advanced groundwater level forecasting with hybrid deep learning model: Tackling water challenges in Taiwan’s largest alluvial fan
Yu-Wen Chang, Wei Sun, Pu-Yun Kow, et al.
Journal of Hydrology (2025), pp. 132887-132887
Closed Access
Time-Series Data-Driven PM2.5 Forecasting: From Theoretical Framework to Empirical Analysis
Chengqian Wu, Ruiyang Wang, Siyu Lu, et al.
Atmosphere (2025) Vol. 16, Iss. 3, pp. 292-292
Open Access
Flood resilience through hybrid deep learning: Advanced forecasting for Taipei's urban drainage system
Li‐Chiu Chang, Ming-Ting Yang, Fi‐John Chang
Journal of Environmental Management (2025) Vol. 379, pp. 124835-124835
Closed Access
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