An Efficient Method for Capturing the High Peak Concentrations of PM<sub>2.5</sub> Using Gaussian-Filtered Deep Learning
This paper proposes a deep learning model that integrates a convolutional neural network with a gate circulation unit that captures patterns of high-peak PM<sub>2.5</sub> concentrations. The purpose is to accurately predict high-peak PM<sub>2.5</sub> concentration data that c...
Guardado en:
Autores principales: | Inchoon Yeo, Yunsoo Choi |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/c0f60351b6164ad097f32db20f70547e |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Estimation of the PM<sub>2.5</sub> and PM<sub>10</sub> Mass Concentration over Land from FY-4A Aerosol Optical Depth Data
por: Yuxin Sun, et al.
Publicado: (2021) -
The Process and Platform for Predicting PM<sub>2.5</sub> Inhalation and Retention during Exercise
por: Hui-Chin Wu, et al.
Publicado: (2021) -
Spatial-Temporal Variation of Air PM<sub>2.5</sub> and PM<sub>10</sub> within Different Types of Vegetation during Winter in an Urban Riparian Zone of Shanghai
por: Jing Wang, et al.
Publicado: (2021) -
Estimation of PM<sub>2.5</sub> Concentration Using Deep Bayesian Model Considering Spatial Multiscale
por: Xingdi Chen, et al.
Publicado: (2021) -
Variations in Nocturnal Residual Layer Height and Its Effects on Surface PM<sub>2.5</sub> over Wuhan, China
por: Xin Ma, et al.
Publicado: (2021)