Spatiotemporal integration of GCN and E-LSTM networks for PM2.5 forecasting

Author:

Kamali Mohammadzadeh Ali,Salah Halima,Jahanmahin Roohollah,Hussain Abd E Ali,Masoud SaraORCID,Huang Yaoxian

Publisher

Elsevier BV

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference50 articles.

1. Analysis and modelling of PM2. 5 temporal and spatial behaviors in European cities;Adães;Sustainability,2019

2. A hybrid approach to estimating national scale spatiotemporal variability of PM2. 5 in the contiguous United States;Beckerman;Environmental science & technology,2013

3. A new framework for analysis of the morphological spatial patterns of urban green space to reduce PM2. 5 pollution: A case study in Wuhan, China;Bi;Sustainable Cities and Society,2022

4. Image-based air quality analysis using deep convolutional neural network;Chakma,2017

5. Chen, H.C., Trinanda Putra K., and Chun-WeiLin J.. "A novel prediction approach for exploring PM2. 5 spatiotemporal propagation based on convolutional recursive neural networks." arXiv preprint arXiv:2101.06213 (2021).

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2. Prediction of PM2.5 Concentration Based on Deep Learning, Multi-Objective Optimization, and Ensemble Forecast;Sustainability;2024-05-30

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