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Air quality prediction based on Long Short-Term Memory Model with advanced feature selection and hyperparameter optimization.

Authors :
Wu, Huiyong
Yang, Tongtong
Wu, Harris
Li, Hongkun
Zhou, Ziwei
Source :
Journal of Intelligent & Fuzzy Systems; 2023, Vol. 45 Issue 4, p5971-5985, 15p
Publication Year :
2023

Abstract

Good air quality is one of the prerequisites for stable urban economic growth and sustainable development. Air quality is influenced by a range of environmental elements. In this study, seven common air pollutants and six kinds of meteorological data in a major city in China are studied. In this urban setting, the air quality index will be estimated based on a Long Short-term Memory (LSTM)model. To improve prediction accuracy, the Random Forest (RF) method is adopted to choose important features and pass them to the LSTM model as input, an improved sparrow search algorithm (ISSA) is used to optimize the hyperparameters of the LSTM model. According to the experimental findings, the RF-ISSA-LSTM model demonstrates superior accuracy compared to both the basic LSTM model and the ISSA-LSTM fusion model. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10641246
Volume :
45
Issue :
4
Database :
Complementary Index
Journal :
Journal of Intelligent & Fuzzy Systems
Publication Type :
Academic Journal
Accession number :
173420200
Full Text :
https://doi.org/10.3233/JIFS-232308