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Artificial neural networks and fuzzy time series forecasting: an application to air quality

The arising air pollution has addressed much attention globally due to its detrimental effects on human health and environment. As an early warning system for air quality control and management, it is important to provide precise information about the future concentrations in pollutants. We present...

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Main Authors: Abd. Rahman, Nur Haizum, Lee, Muhammad Hisyam, Suhartono, Suhartono, Latif, Mohd. Talib
Format: Article
Published: Kluwer Academic Publishers 2015
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/57896/
http://dx.doi.org/10.1007/s11135-014-0132-6
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spelling my.utm.578962021-08-02T13:25:49Z http://eprints.utm.my/id/eprint/57896/ Artificial neural networks and fuzzy time series forecasting: an application to air quality Abd. Rahman, Nur Haizum Lee, Muhammad Hisyam Suhartono, Suhartono Latif, Mohd. Talib QA Mathematics The arising air pollution has addressed much attention globally due to its detrimental effects on human health and environment. As an early warning system for air quality control and management, it is important to provide precise information about the future concentrations in pollutants. We present here a time series model in predicting the Air Pollution Index (API) from three different stations; industrial, residential, and sub-urban areas between 2000 and 2009. In this paper, the Box–Jenkins approach of seasonal autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and three models of fuzzy time series (FTS) have been compared by using the mean absolute percentage error, mean absolute error, mean square error, and root mean square error. Although all the methods were used as operational tools, the ANN seemed more accurate in forecasting API. The results showed that FTS (i.e. Chen’s, Yu’s, and Cheng’s) performed inconsistent results since the conventional methods of ARIMA outperformed the performance of FTS. However, consistent results were achieved as the ANNs gave the smallest forecasting error compared to FTS and ARIMA. Kluwer Academic Publishers 2015 Article PeerReviewed Abd. Rahman, Nur Haizum and Lee, Muhammad Hisyam and Suhartono, Suhartono and Latif, Mohd. Talib (2015) Artificial neural networks and fuzzy time series forecasting: an application to air quality. Quality & Quantity, 49 (6). pp. 2633-2647. ISSN 3346-4472 http://dx.doi.org/10.1007/s11135-014-0132-6 DOI:10.1007/s11135-014-0132-6
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA Mathematics
spellingShingle QA Mathematics
Abd. Rahman, Nur Haizum
Lee, Muhammad Hisyam
Suhartono, Suhartono
Latif, Mohd. Talib
Artificial neural networks and fuzzy time series forecasting: an application to air quality
description The arising air pollution has addressed much attention globally due to its detrimental effects on human health and environment. As an early warning system for air quality control and management, it is important to provide precise information about the future concentrations in pollutants. We present here a time series model in predicting the Air Pollution Index (API) from three different stations; industrial, residential, and sub-urban areas between 2000 and 2009. In this paper, the Box–Jenkins approach of seasonal autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and three models of fuzzy time series (FTS) have been compared by using the mean absolute percentage error, mean absolute error, mean square error, and root mean square error. Although all the methods were used as operational tools, the ANN seemed more accurate in forecasting API. The results showed that FTS (i.e. Chen’s, Yu’s, and Cheng’s) performed inconsistent results since the conventional methods of ARIMA outperformed the performance of FTS. However, consistent results were achieved as the ANNs gave the smallest forecasting error compared to FTS and ARIMA.
format Article
author Abd. Rahman, Nur Haizum
Lee, Muhammad Hisyam
Suhartono, Suhartono
Latif, Mohd. Talib
author_facet Abd. Rahman, Nur Haizum
Lee, Muhammad Hisyam
Suhartono, Suhartono
Latif, Mohd. Talib
author_sort Abd. Rahman, Nur Haizum
title Artificial neural networks and fuzzy time series forecasting: an application to air quality
title_short Artificial neural networks and fuzzy time series forecasting: an application to air quality
title_full Artificial neural networks and fuzzy time series forecasting: an application to air quality
title_fullStr Artificial neural networks and fuzzy time series forecasting: an application to air quality
title_full_unstemmed Artificial neural networks and fuzzy time series forecasting: an application to air quality
title_sort artificial neural networks and fuzzy time series forecasting: an application to air quality
publisher Kluwer Academic Publishers
publishDate 2015
url http://eprints.utm.my/id/eprint/57896/
http://dx.doi.org/10.1007/s11135-014-0132-6
_version_ 1707765834292658176
score 13.211869

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