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

• Published in: Quality & quantity 2015-11, Vol.49 (6), p.2633-2647
• Main Authors: Rahman, Nur Haizum Abd, Lee, Muhammad Hisyam, Suhartono, Latif, Mohd Talib
• Format: Article
• Language: English
• Subjects: Air pollution; Air quality; Air quality indexes; Air quality management; Datasets; Decision making; Environmental monitoring; Forecasting; Forecasts and trends; Fuzzy sets; Health aspects; Mathematical models; Mean square errors; Methodology of the Social Sciences; Neural networks; Nitrogen dioxide; Outdoor air quality; Pollutants; Public health; Quality management; Science; Social Sciences; Studies; Suburban areas; Time series; Variables
• ISSN: 0033-5177; 1573-7845
• DOI: 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.