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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/2792481 |
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| author | Marco Antonio Aceves-Fernández Ricardo Domínguez-Guevara Jesus Carlos Pedraza-Ortega José Emilio Vargas-Soto |
| author_facet | Marco Antonio Aceves-Fernández Ricardo Domínguez-Guevara Jesus Carlos Pedraza-Ortega José Emilio Vargas-Soto |
| author_sort | Marco Antonio Aceves-Fernández |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1a0cfaba0f96417885a979dd451f695a |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-1a0cfaba0f96417885a979dd451f695a2025-02-03T01:27:03ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/27924812792481Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) PredictionMarco Antonio Aceves-Fernández0Ricardo Domínguez-Guevara1Jesus Carlos Pedraza-Ortega2José Emilio Vargas-Soto3Faculty of Engineering, Autonomous University of Queretaro, 76010 Queretaro, MexicoFaculty of Engineering, Autonomous University of Queretaro, 76010 Queretaro, MexicoFaculty of Engineering, Autonomous University of Queretaro, 76010 Queretaro, MexicoFaculty of Engineering, Autonomous University of Queretaro, 76010 Queretaro, MexicoParticulate 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.http://dx.doi.org/10.1155/2020/2792481 |
| spellingShingle | Marco Antonio Aceves-Fernández Ricardo Domínguez-Guevara Jesus Carlos Pedraza-Ortega José Emilio Vargas-Soto Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction Discrete Dynamics in Nature and Society |
| title | Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction |
| title_full | Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction |
| title_fullStr | Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction |
| title_full_unstemmed | Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction |
| title_short | Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction |
| title_sort | evaluation of key parameters using deep convolutional neural networks for airborne pollution pm10 prediction |
| url | http://dx.doi.org/10.1155/2020/2792481 |
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