Deep-AIR: A Hybrid CNN-LSTM Framework for Fine-Grained Air Pollution Estimation and Forecast in Metropolitan Cities

Deep-AIR: A Hybrid CNN-LSTM Framework for Fine-Grained Air Pollution Estimation and Forecast in Metropolitan Cities

Air pollution presents a serious health challenge in urban metropolises. While accurately monitoring and forecasting air pollution are highly crucial, existing data-driven models have yet fully captured the complex interactions between the temporal characteristics of air pollution and the spatial ch...

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Bibliographic Details
Main Authors: Qi Zhang, Yang Han, Victor O. K. Li, Jacqueline C. K. Lam
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Fine-grained air pollution estimation and forecast
spatial-temporal data
deep learning
CNN-LSTM
street canyon effect
traffic speed
Online Access:https://ieeexplore.ieee.org/document/9780279/

Internet

https://ieeexplore.ieee.org/document/9780279/

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