Hybrid seasonal ARIMA and artificial neural network in forecasting southeast asia city air pollutant index
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Academy of Sciences Malaysia
2019
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| Online Access: | http://psasir.upm.edu.my/id/eprint/80111/1/Hybrid%20Seasonal%20ARIMA%20and%20Artificial%20Neural%20Network%20in%20Forecasting%20Southeast%20Asia%20City%20Air%20Pollutant%20Index.pdf |
| _version_ | 1796980726827057152 |
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| author | Abd Rahman, Nur Haizum Lee, Muhammad Hisyam Suhartono Latif, Mohd Talib |
| author_facet | Abd Rahman, Nur Haizum Lee, Muhammad Hisyam Suhartono Latif, Mohd Talib |
| author_sort | Abd Rahman, Nur Haizum |
| collection | UPM |
| description | 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. |
| first_indexed | 2024-03-06T10:27:18Z |
| format | Article |
| id | upm.eprints-80111 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T10:27:18Z |
| publishDate | 2019 |
| publisher | Academy of Sciences Malaysia |
| record_format | dspace |
| spelling | upm.eprints-801112020-09-22T03:14:24Z http://psasir.upm.edu.my/id/eprint/80111/ Hybrid seasonal ARIMA and artificial neural network in forecasting southeast asia city air pollutant index Abd Rahman, Nur Haizum Lee, Muhammad Hisyam Suhartono Latif, Mohd Talib 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. Academy of Sciences Malaysia 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/80111/1/Hybrid%20Seasonal%20ARIMA%20and%20Artificial%20Neural%20Network%20in%20Forecasting%20Southeast%20Asia%20City%20Air%20Pollutant%20Index.pdf 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 https://www.akademisains.gov.my/asmsj/article/hybrid-seasonal-arima-and-artificial-neural-network-in-forecasting-southeast-asia-city-air-pollutant-index/ |
| spellingShingle | Abd Rahman, Nur Haizum Lee, Muhammad Hisyam Suhartono Latif, Mohd Talib Hybrid seasonal ARIMA and artificial neural network in forecasting southeast asia city air pollutant index |
| title | Hybrid seasonal ARIMA and artificial neural network in forecasting southeast asia city air pollutant index |
| title_full | Hybrid seasonal ARIMA and artificial neural network in forecasting southeast asia city air pollutant index |
| title_fullStr | Hybrid seasonal ARIMA and artificial neural network in forecasting southeast asia city air pollutant index |
| title_full_unstemmed | Hybrid seasonal ARIMA and artificial neural network in forecasting southeast asia city air pollutant index |
| title_short | Hybrid seasonal ARIMA and artificial neural network in forecasting southeast asia city air pollutant index |
| title_sort | hybrid seasonal arima and artificial neural network in forecasting southeast asia city air pollutant index |
| url | http://psasir.upm.edu.my/id/eprint/80111/1/Hybrid%20Seasonal%20ARIMA%20and%20Artificial%20Neural%20Network%20in%20Forecasting%20Southeast%20Asia%20City%20Air%20Pollutant%20Index.pdf |
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