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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 balanced social LSTM for PM2.5 concentration prediction based on local spatiotemporal correlation
Lukui Shi, Huizhen Zhang, Xia Xu, et al.
Chemosphere (2021) Vol. 291, pp. 133124-133124
Closed Access | Times Cited: 30

Showing 1-25 of 30 citing articles:

VAR-tree model based spatio-temporal characterization and prediction of O3 concentration in China
Hongbin Dai, Guangqiu Huang, Jingjing Wang, et al.
Ecotoxicology and Environmental Safety (2023) Vol. 257, pp. 114960-114960
Open Access | Times Cited: 53
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: 18
Application of complete ensemble empirical mode decomposition based multi-stream informer (CEEMD-MsI) in PM2.5 concentration long-term prediction
Qinghe Zheng, Xinyu Tian, Zhiguo Yu, et al.
Expert Systems with Applications (2023) Vol. 245, pp. 123008-123008
Closed Access | Times Cited: 28
Interpreting hourly mass concentrations of PM2.5 chemical components with an optimal deep-learning model
Hongyi Li, Ting Yang, Yiming Du, et al.
Journal of Environmental Sciences (2024) Vol. 151, pp. 125-139
Closed Access | Times Cited: 7
Short-Term PM2.5 Prediction Based on Multi-Modal Meteorological Data for Consumer-Grade Meteorological Electronic Systems
Lina Wang, Xiaochen Jin, Zengyang Huang, et al.
IEEE Transactions on Consumer Electronics (2024) Vol. 70, Iss. 1, pp. 3464-3474
Closed Access | Times Cited: 4
Dynamic prediction of PM2.5 concertation in China using experience replay with multi-period memory buffers
Haoze Shi, Xin Yang, Hong Tang, et al.
Atmospheric Research (2025), pp. 108063-108063
Closed Access
Spatio-Temporal Characteristics of PM2.5 Concentrations in China Based on Multiple Sources of Data and LUR-GBM during 2016–2021
Hongbin Dai, Guangqiu Huang, Jingjing Wang, et al.
International Journal of Environmental Research and Public Health (2022) Vol. 19, Iss. 10, pp. 6292-6292
Open Access | Times Cited: 20
New PM2.5 forecasting system based on combined neural network and an improved multi-objective optimization algorithm: Taking the economic belt surrounding the Bohai Sea as an example
Zhirui Tian, Gai Mei
Journal of Cleaner Production (2022) Vol. 375, pp. 134048-134048
Closed Access | Times Cited: 20
Predicting indoor particle concentration in mechanically ventilated classrooms using neural networks: Model development and generalization ability analysis
Jianlin Ren, Junjie He, Atila Novoselac
Building and Environment (2023) Vol. 238, pp. 110404-110404
Closed Access | Times Cited: 10
Predicting short-term PM 2.5 concentrations at fine temporal resolutions using a multi-branch temporal graph convolutional neural network
Qingfeng Guan, Jingyi Wang, Shuliang Ren, et al.
International Journal of Geographical Information Science (2024) Vol. 38, Iss. 4, pp. 778-801
Closed Access | Times Cited: 3
State-of-art in modelling particulate matter (PM) concentration: a scoping review of aims and methods
Lorenzo Gianquintieri, Daniele Oxoli, Enrico G. Caiani, et al.
Environment Development and Sustainability (2024)
Open 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
Comparative Analysis of Multiple Deep Learning Models for Forecasting Monthly Ambient PM2.5 Concentrations: A Case Study in Dezhou City, China
Zhenfang He, Qingchun Guo
Atmosphere (2024) Vol. 15, Iss. 12, pp. 1432-1432
Open Access | Times Cited: 3
A combined prediction system for PM2.5 concentration integrating spatio-temporal correlation extracting, multi-objective optimization weighting and non-parametric estimation
Jianzhou Wang, Yuansheng Qian, Yuyang Gao, et al.
Atmospheric Pollution Research (2023) Vol. 14, Iss. 10, pp. 101880-101880
Closed Access | Times Cited: 8
A new cross-domain prediction model of air pollutant concentration based on secure federated learning and optimized LSTM neural network
Guangqiu Huang, Xixuan Zhao, LU Qiu-qin
Environmental Science and Pollution Research (2022) Vol. 30, Iss. 2, pp. 5103-5125
Closed Access | Times Cited: 13
PM2.5 concentration prediction using weighted CEEMDAN and improved LSTM neural network
Li Zhang, Jinlan Liu, Yuhan Feng, et al.
Environmental Science and Pollution Research (2023) Vol. 30, Iss. 30, pp. 75104-75115
Open Access | Times Cited: 7
A new hybrid deep neural network for multiple sites PM2.5 forecasting
Mengfan Teng, Siwei Li, Jie Yang, et al.
Journal of Cleaner Production (2024) Vol. 473, pp. 143542-143542
Closed Access | Times Cited: 2
Prediction of PM2.5 Concentration Based on Deep Learning for High-Dimensional Time Series
Jie Hu, Jia Yuan, Zhenhong Jia, et al.
Applied Sciences (2024) Vol. 14, Iss. 19, pp. 8745-8745
Open Access | Times Cited: 2
Smart solutions for urban health risk assessment: A PM2.5 monitoring system incorporating spatiotemporal long-short term graph convolutional network
Roberto Chang, Shahzeb Tariq, Jorge Loy-Benitez, et al.
Chemosphere (2023) Vol. 335, pp. 139071-139071
Closed Access | Times Cited: 6
A Spatial–Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2.5 Concentration Prediction
Shaofu Lin, Junjie Zhao, Jianqiang Li, et al.
Entropy (2022) Vol. 24, Iss. 8, pp. 1125-1125
Open Access | Times Cited: 9
CombineDeepNet: A Deep Network for Multistep Prediction of Near-Surface PM$_{2.5}$ Concentration
Prasanjit Dey, Soumyabrata Dev, Bianca Schoen Phelan
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2023) Vol. 17, pp. 788-807
Open Access | Times Cited: 4
Forecasting Fine Particulate Matter Concentrations by In-Depth Learning Model According to Random Forest and Bilateral Long- and Short-Term Memory Neural Networks
Jie Zhao, Linjiang Yuan, Kun Sun, et al.
Sustainability (2022) Vol. 14, Iss. 15, pp. 9430-9430
Open Access | Times Cited: 7
Fine-Grained Individual Air Quality Index (IAQI) Prediction Based on Spatial-Temporal Causal Convolution Network: A Case Study of Shanghai
Xiliang Liu, Junjie Zhao, Shaofu Lin, et al.
Atmosphere (2022) Vol. 13, Iss. 6, pp. 959-959
Open Access | Times Cited: 5
Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study
Chao Liu, Mingshuang Xu, Yufeng Liu, et al.
International Journal of Environmental Research and Public Health (2022) Vol. 19, Iss. 23, pp. 15612-15612
Open Access | Times Cited: 3
Prediction of heavy metal and PM2.5 concentrations in atmospheric particulate matter using key magnetic parameters
Guan Wang, Zhenxiang Ji, Xun Tian, et al.
Air Quality Atmosphere & Health (2024)
Closed Access
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