Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction
Particulate matter with a diameter less than 10 micrometers (PM10) is today an important subject of study, mainly because of its increasing concentration and its impact on environment and public health. This article summarizes the usage of convolutional neural networks (CNNs) to forecast PM10 concen...
Saved in:
Main Authors: | Marco Antonio Aceves-Fernández, Ricardo Domínguez-Guevara, Jesus Carlos Pedraza-Ortega, José Emilio Vargas-Soto |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2020-01-01
|
Series: | Discrete Dynamics in Nature and Society |
Online Access: | http://dx.doi.org/10.1155/2020/2792481 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Emerging Measurement Techniques for Airborne Pollutants
by: Ki-Hyun Kim, et al.
Published: (2011-01-01) -
PM10 AIR POLLUTION IN MASHAD CITY USING ARTIFICIAL NEURAL NETWORK AND MAKOV CHAIN MODEL
Published: (2017-12-01) -
Seasonal Characteristics, Sources and Pollution Pathways of PM10 at High Altitudes Himalayas of India
by: Nikki Choudhary, et al.
Published: (2022-05-01) -
How much inequality in exposure to high PM10 pollution is too much to be considered environmentally unfair? An assessment for vulnerable groups in two major Spanish cities
by: Antonio Moreno Jiménez, et al.
Published: (2022-03-01) -
An Airborne Gravity Gradient Compensation Method Based on Convolutional and Long Short-Term Memory Neural Networks
by: Shuai Zhou, et al.
Published: (2025-01-01)