Statistical quality control: A case study research
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Air quality prediction using a novel three-stage model based on time series decomposition
- Published: 09 May 2024
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- Mingyue Sun 1 ,
- Congjun Rao 1 &
- Zhuo Hu 2
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Air pollution control is one of the most important aspects of sustainable development in modern times, in which air quality prediction is the key in the process of governance. How to further improve the accuracy of air quality prediction and provide more powerful decision support for relevant departments is the main task of this paper. The air quality index (AQI) is a comprehensive indicator used to measure the air quality level. Taking AQI as the research object, this paper establishes a three-stage prediction model based on time series decomposition with improved particle swarm optimization—back propagation neural network (IPSO-BPNN), and makes empirical analysis of AQI in Wuhan City, China from 2014 to 2023. Specifically, at the first stage, the raw data is decomposed into season, trend, cycle and error subsequences by means of seasonal decomposition and empirical mode decomposition. At the second stage, the adaptive formula for the inertia weight in the particle swarm algorithm is newly defined by combining the sigmoid function with the scaling factor and control factor, and the improved particle swarm algorithm is used to optimize the initial weights and thresholds of BP neutral network. The cycle and error subsequences are predicted using this hybrid model. At the last stage, the four subsequences are recombined to obtain predictions of AQI. The empirical analysis results show that the MAPE of this hybrid prediction model reaches 0.04823, which is lower than the other two comparative models. The experimental results prove that the hybrid prediction model proposed in this paper can make an effective prediction.
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Data availability
The datasets generated and analyzed during the current study are available in the [China Air Quality Online Monitoring and Analysis Platform] repository, [ https://www.aqistudy.cn/historydata/ ].
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Acknowledgements
We would like to thank the editor and the anonymous reviewers for their helpful comments.
This work is supported by the National Undergraduate Innovation and Entrepreneurship Training Program (No. 202310497065), and the National Natural Science Foundation of China (No. 72071150).
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Mingyue Sun & Congjun Rao
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MS: Software, Visualization, Investigation, Writing-Original draft preparation. CR: Conceptualization, Methodology, Data Curation. ZH: Formal analysis, Writing- Reviewing and Editing.
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Correspondence to Congjun Rao .
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Sun, M., Rao, C. & Hu, Z. Air quality prediction using a novel three-stage model based on time series decomposition. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-04955-1
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Received : 22 March 2023
Accepted : 18 April 2024
Published : 09 May 2024
DOI : https://doi.org/10.1007/s10668-024-04955-1
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