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Article type: Research Article
Authors: Liu, Kaia | Wang, Mingyib; *
Affiliations: [a] Researching Office of City Situation and Development, Party School of the CPC Lvliang Municipal Committee, Lvliang, China | [b] Guangzhou Research Institute of Optical, Mechanical and Electronical Technologies Co., Ltd, Guangzhou, China
Correspondence: [*] Corresponding author. Mingyi Wang, Guangzhou Research Institute of Optical, Mechanical and Electronical Technologies Co., Ltd, Guangzhou 510000, China. E-mail: [email protected].
Abstract: China has emerged as one of the nations with the worst air pollution in recent years. The severe air pollution has caused a large number of population migration and also caused serious economic problems. Since the concentration of air pollutants can change quickly in a short amount of time, the study first tracked PM2.5, PM10, NO2, CO, SO2, and O3 as targets before using the particle swarm optimization algorithm to improve the PIO algorithm, which is based on the traditional pigeon swarm algorithm. To estimate the concentration of air pollutants, combine the wavelet packet decomposition technique, MDS visualization method, and k-means algorithm. Then, apply the enhanced PIO algorithm to optimize the ELM algorithm. Finally, a new type of decomposition-optimization-clustering-integration hybrid learning model, namely DOCIAPC model, is constructed. The experimental findings indicate that, when predicting the concentration of various air pollutants, the DOCIAPC model’s average direction prediction accuracy is 90.37% . In conclusion, the model suggested in the study has excellent performance and applicability, and it can accurately predict the concentration of air pollutants, help the government take action to reduce air pollution, balance the environment and economy, as well as the allocation of labor and its resources in the city.
Keywords: Air pollution, wavelet packet decomposition, pigeon group algorithm, K-means algorithm, MDS, labor force
DOI: 10.3233/JIFS-235902
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
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