Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction
Joint Authors
Aceves-Fernandez, M. A.
Pedraza-Ortega, J. C.
Domínguez-Guevara, Ricardo
Vargas-Soto, José Emilio
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-02-10
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
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 concentrations based on atmospheric variables.
In this particular case-study, the use of deep convolutional neural networks (both 1D and 2D) was explored to probe the feasibility of these techniques in prediction tasks.
Furthermore, in this contribution, an ensemble method called Bagging (BEM) is used to improve the accuracy of the prediction model.
Lastly, a well-known technique for PM10 forecasting, called multilayer perceptron (MLP) is used as a comparison to show the feasibility, accuracy, and robustness of the proposed model.
In this contribution, it was found that the CNNs outperforms MLP, especially when they are executed using ensemble models.
American Psychological Association (APA)
Aceves-Fernandez, M. A.& Domínguez-Guevara, Ricardo& Pedraza-Ortega, J. C.& Vargas-Soto, José Emilio. 2020. Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1152918
Modern Language Association (MLA)
Aceves-Fernandez, M. A.…[et al.]. Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1152918
American Medical Association (AMA)
Aceves-Fernandez, M. A.& Domínguez-Guevara, Ricardo& Pedraza-Ortega, J. C.& Vargas-Soto, José Emilio. Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1152918
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1152918