UPM Institutional Repository

Hybrid seasonal ARIMA and artificial neural network in forecasting southeast asia city air pollutant index


Abd Rahman, Nur Haizum and Lee, Muhammad Hisyam and Suhartono and Latif, Mohd Talib (2019) Hybrid seasonal ARIMA and artificial neural network in forecasting southeast asia city air pollutant index. ASM Science Journal, 12 (spec.1). pp. 215-226. ISSN 1823-6782


The rise of air pollution has received much attention globally. As an early warning system for air quality control and management, it is important to provide precise future concentrations pollutant information. Using time series forecasting methods, the forecast of daily Air Pollutant Index (API) is presented here. The hybrid method between seasonal autoregressive integrated moving average (SARIMA) and artificial neural network (ANN) are chosen. To verify, the accuracies are measured using error magnitude approach. However, evaluation of forecasting API is also in uenced by the health classification based on the threshold value assigned in air quality guidelines. Thus, forecast accuracies based on index value, namely as true predicted rate (TPR), false positive rate (FPR), false alarm rate (FAR) and successful index (SI) are also used for forecast validation. As shown in the results, the hybrid model performs better in both model's evaluations group used. Hence, the hybrid method must be considered in the forecasting area due to the capability to analyze real data consisting of both linear and nonlinear patterns. Besides, using the appropriate measurement in accordance to the purpose of forecasting is important to produce an accurate forecast.

Download File

[img] Text
Hybrid Seasonal ARIMA and Artificial Neural Network in Forecasting Southeast Asia City Air Pollutant Index.pdf
Restricted to Repository staff only

Download (1MB)

Additional Metadata

Item Type: Article
Divisions: Faculty of Science
Publisher: Academy of Sciences Malaysia
Keywords: Air pollutant index (API); Artificial neural network; Forecasting evaluation; Hybrid; SARIMA
Depositing User: Azhar Abdul Rahman
Date Deposited: 22 Sep 2020 03:14
Last Modified: 22 Sep 2020 03:14
URI: http://psasir.upm.edu.my/id/eprint/80111
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item