Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering

[Display omitted] •The AQI forecasting model uses deep learning and spatiotemporal clustering.•The multiple-site forecasting models were developed for the next 1–6 h.•The overall forecasting for all the stations in Beijing through LSTM is optimal.•Seasonal or spatial clustering-based forecasting is...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Expert systems with applications 2021-05, Vol.169, p.114513, Article 114513
Hauptverfasser:	Yan, Rui, Liao, Jiaqiang, Yang, Jie, Sun, Wei, Nong, Mingyue, Li, Feipeng
Format:	Artikel
Sprache:	eng
Schlagworte:	Air pollution Air quality AQI Artificial neural networks Back propagation networks Clustering CNN Forecasting LSTM Mathematical models Neural networks Outdoor air quality Spatiotemporal clustering
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!