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

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

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Official URL: http://dx.doi.org/10.1007/s11135-014-0132-6


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.

Item Type:Article
Uncontrolled Keywords:air pollution index (api),arima,artificial neural network,forecasting,fuzzy time series,time series
Subjects:A General Works
ID Code:57896
Deposited By: Haliza Zainal
Deposited On:04 Dec 2016 12:07
Last Modified:08 Feb 2017 16:37

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