Air quality prediction model based on mRMR-RF feature selection and ISSA-LSTM.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
07 Aug 2023
07 Aug 2023
Historique:
received:
21
04
2023
accepted:
31
07
2023
medline:
8
8
2023
pubmed:
8
8
2023
entrez:
7
8
2023
Statut:
epublish
Résumé
Severe air pollution poses a significant threat to public safety and human health. Predicting future air quality conditions is crucial for implementing pollution control measures and guiding residents' activity choices. However, traditional single-module machine learning models suffer from long training times and low prediction accuracy. To improve the accuracy of air quality forecasting, this paper proposes a ISSA-LSTM model-based approach for predicting the air quality index (AQI). The model consists of three main components: random forest (RF) and mRMR, improved sparrow search algorithm (ISSA), and long short-term memory network (LSTM). Firstly, RF-mRMR is used to select the influential variables affecting AQI, thereby enhancing the model's performance. Next, ISSA algorithm is employed to optimize the hyperparameters of LSTM, further improving the model's performance. Finally, LSTM model is utilized to predict AQI concentrations. Through comparative experiments, it is demonstrated that the ISSA-LSTM model outperforms other models in terms of RMSE and R
Identifiants
pubmed: 37550459
doi: 10.1038/s41598-023-39838-4
pii: 10.1038/s41598-023-39838-4
pmc: PMC10406845
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
12825Informations de copyright
© 2023. Springer Nature Limited.
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