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Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong

With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward...

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Detalles Bibliográficos
Autores principales: Zhang, Jiangshe, Ding, Weifu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5334668/
https://www.ncbi.nlm.nih.gov/pubmed/28125034
http://dx.doi.org/10.3390/ijerph14020114
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author Zhang, Jiangshe
Ding, Weifu
author_facet Zhang, Jiangshe
Ding, Weifu
author_sort Zhang, Jiangshe
collection PubMed
description With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with [Formula: see text] increased and root mean square error values decreased respectively.
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spelling pubmed-53346682017-03-16 Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong Zhang, Jiangshe Ding, Weifu Int J Environ Res Public Health Article With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with [Formula: see text] increased and root mean square error values decreased respectively. MDPI 2017-01-24 2017-02 /pmc/articles/PMC5334668/ /pubmed/28125034 http://dx.doi.org/10.3390/ijerph14020114 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Jiangshe
Ding, Weifu
Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong
title Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong
title_full Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong
title_fullStr Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong
title_full_unstemmed Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong
title_short Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong
title_sort prediction of air pollutants concentration based on an extreme learning machine: the case of hong kong
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5334668/
https://www.ncbi.nlm.nih.gov/pubmed/28125034
http://dx.doi.org/10.3390/ijerph14020114
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