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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:

Application of decomposition-ensemble learning paradigm with phase space reconstruction for day-ahead PM 2.5 concentration forecasting
Mingfei Niu, Kai Gan, Shaolong Sun, et al.
Journal of Environmental Management (2017) Vol. 196, pp. 110-118
Closed Access | Times Cited: 80

Showing 1-25 of 80 citing articles:

Driven by Data or Derived Through Physics? A Review of Hybrid Physics Guided Machine Learning Techniques With Cyber-Physical System (CPS) Focus
Rahul Rai, Chandan K. Sahu
IEEE Access (2020) Vol. 8, pp. 71050-71073
Open Access | Times Cited: 216
The Forecasting of PM2.5 Using a Hybrid Model Based on Wavelet Transform and an Improved Deep Learning Algorithm
Weibiao Qiao, Wencai Tian, Yu Tian, et al.
IEEE Access (2019) Vol. 7, pp. 142814-142825
Open Access | Times Cited: 182
A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting
Pei Du, Jianzhou Wang, Hao Yan, et al.
Applied Soft Computing (2020) Vol. 96, pp. 106620-106620
Open Access | Times Cited: 137
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: 125
Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters
Elham Fijani, Rahim Barzegar, Ravinesh C. Deo, et al.
The Science of The Total Environment (2018) Vol. 648, pp. 839-853
Closed Access | Times Cited: 157
A decomposition-clustering-ensemble learning approach for solar radiation forecasting
Shaolong Sun, Shouyang Wang, Guowei Zhang, et al.
Solar Energy (2018) Vol. 163, pp. 189-199
Closed Access | Times Cited: 144
A SVR–ANN combined model based on ensemble EMD for rainfall prediction
Yu Xiang, Ling Gou, Lihua He, et al.
Applied Soft Computing (2018) Vol. 73, pp. 874-883
Closed Access | Times Cited: 142
A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting
Mingfei Niu, Yueyong Hu, Shaolong Sun, et al.
Applied Mathematical Modelling (2018) Vol. 57, pp. 163-178
Closed Access | Times Cited: 130
An innovative hybrid model based on outlier detection and correction algorithm and heuristic intelligent optimization algorithm for daily air quality index forecasting
Jianzhou Wang, Pei Du, Hao Yan, et al.
Journal of Environmental Management (2019) Vol. 255, pp. 109855-109855
Closed Access | Times Cited: 112
A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition
Xike Zhang, Qiuwen Zhang, Gui Zhang, et al.
International Journal of Environmental Research and Public Health (2018) Vol. 15, Iss. 5, pp. 1032-1032
Open Access | Times Cited: 93
Air PM2.5 concentration multi-step forecasting using a new hybrid modeling method: Comparing cases for four cities in China
Hui Liu, Kairong Jin, Zhu Duan
Atmospheric Pollution Research (2019) Vol. 10, Iss. 5, pp. 1588-1600
Closed Access | Times Cited: 83
COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts
Mingyun Gao, Honglin Yang, Qinzi Xiao, et al.
Socio-Economic Planning Sciences (2022) Vol. 83, pp. 101228-101228
Open Access | Times Cited: 63
24-Hour prediction of PM2.5 concentrations by combining empirical mode decomposition and bidirectional long short-term memory neural network
Mengfan Teng, Siwei Li, Jia Xing, et al.
The Science of The Total Environment (2022) Vol. 821, pp. 153276-153276
Open Access | Times Cited: 42
Air quality forecasting with artificial intelligence techniques: A scientometric and content analysis
Yanzhao Li, Ju’e Guo, Shaolong Sun, et al.
Environmental Modelling & Software (2022) Vol. 149, pp. 105329-105329
Closed Access | Times Cited: 41
Investor sentiment and machine learning: Predicting the price of China's crude oil futures market
Zhe Jiang, Lin Zhang, Lingling Zhang, et al.
Energy (2022) Vol. 247, pp. 123471-123471
Closed Access | Times Cited: 39
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
Prediction method of PM2.5 concentration based on decomposition and integration
Hong Yang, Wenqian Wang, Guohui Li
Measurement (2023) Vol. 216, pp. 112954-112954
Closed 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: 21
Research and application of a novel hybrid air quality early-warning system: A case study in China
Chen Li, Zhijie Zhu
The Science of The Total Environment (2018) Vol. 626, pp. 1421-1438
Closed Access | Times Cited: 72
A hybrid multi-resolution multi-objective ensemble model and its application for forecasting of daily PM2.5 concentrations
Hui Liu, Zhu Duan, Chao Chen
Information Sciences (2019) Vol. 516, pp. 266-292
Closed Access | Times Cited: 71
Data multi-scale decomposition strategies for air pollution forecasting: A comprehensive review
Hui Liu, Shi Yin, Chao Chen, et al.
Journal of Cleaner Production (2020) Vol. 277, pp. 124023-124023
Closed Access | Times Cited: 67
Forecasting air passenger demand with a new hybrid ensemble approach
Feng Jin, Yongwu Li, Shaolong Sun, et al.
Journal of Air Transport Management (2019) Vol. 83, pp. 101744-101744
Closed Access | Times Cited: 62
Prediction of outdoor PM2.5 concentrations based on a three-stage hybrid neural network model
Hui Liu, Chao Chen
Atmospheric Pollution Research (2019) Vol. 11, Iss. 3, pp. 469-481
Closed Access | Times Cited: 55
Hourly PM2.5 concentration forecasting based on mode decomposition-recombination technique and ensemble learning approach in severe haze episodes of China
Wei Sun, Zhaoqi Li
Journal of Cleaner Production (2020) Vol. 263, pp. 121442-121442
Closed Access | Times Cited: 53
PM2.5 concentration forecasting: Development of integrated multivariate variational mode decomposition with kernel Ridge regression and weighted mean of vectors optimization
Tao Hai, Iman Ahmadianfar, Leonardo Goliatt, et al.
Atmospheric Pollution Research (2024) Vol. 15, Iss. 6, pp. 102125-102125
Closed Access | Times Cited: 5
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