Real-Time Mechanism Based on Deep Learning Approaches for Analyzing the Impact of Future Timestep Forecasts on Actual Air Quality Index of PM10
The air quality in Jakarta, particularly concerning PM10 particulate matter, has become a serious concern due to its significant impact on health and the environment. The increase in pollution in this city is often triggered by industrial activities and seasonal factors, such as forest fires, which...
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024016864 |
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| author | Furizal Alfian Ma'arif Iswanto Suwarno Alya Masitha Lathifatul Aulia Abdel-Nasser Sharkawy |
| author_facet | Furizal Alfian Ma'arif Iswanto Suwarno Alya Masitha Lathifatul Aulia Abdel-Nasser Sharkawy |
| author_sort | Furizal |
| collection | DOAJ |
| description | The air quality in Jakarta, particularly concerning PM10 particulate matter, has become a serious concern due to its significant impact on health and the environment. The increase in pollution in this city is often triggered by industrial activities and seasonal factors, such as forest fires, which create challenges in air quality management. This study aims to map seasonal patterns in AQI PM10 concentrations and to develop a DL-based air quality prediction system that can operate in real-time, while also considering the impact of future forecasting timesteps on actual values. Additionally, this goal supports effective mitigation efforts and provides new insights for decision-making in addressing urban pollution. We propose an approach using GRU for time series forecasting, combined with an ARIMA model for imputing missing data. Utilizing air quality index data from Jakarta over more than 13 years, this research identifies consistent seasonal patterns, with PM10 concentrations peaking between May and October. Most days in this dataset fall into the “Moderate” category of the AQI, although there is considerable variation influenced by seasonal phenomena and industrial activities. Results show that the GRU model effectively captures fundamental patterns in the AQI PM10 data, although predictive accuracy tends to decrease as the forecasting interval increases. This study also provides new insights into the application of the GRU model for multi-timestep forecasting, focusing on short- and medium-term predictions that can be used as a decision-making tool for urban pollution mitigation. |
| format | Article |
| id | doaj-art-fbc3c9dfd6c74cf6b8d54cafd9adc932 |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
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| series | Results in Engineering |
| spelling | doaj-art-fbc3c9dfd6c74cf6b8d54cafd9adc9322025-08-20T01:58:30ZengElsevierResults in Engineering2590-12302024-12-012410343410.1016/j.rineng.2024.103434Real-Time Mechanism Based on Deep Learning Approaches for Analyzing the Impact of Future Timestep Forecasts on Actual Air Quality Index of PM10 Furizal0Alfian Ma'arif1Iswanto Suwarno2Alya Masitha3Lathifatul Aulia4Abdel-Nasser Sharkawy5Department of Informatics Engineering, Universitas Islam Riau, Pekanbaru, 28284, IndonesiaDepartment of Electrical Engineering, Universitas Ahmad Dahlan, Yogyakarta, 55191, Indonesia; Correspondence.Department of Electrical Engineering, Universitas Muhammadiyah Yogyakarta, Yogyakarta, 55183, IndonesiaDepartment of Software Engineering, Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang, Semarang, 50185, IndonesiaDepartment of Actuarial Science, Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang, Semarang, 50185, IndonesiaMechanical Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt; Mechanical Engineering Department, College of Engineering, Fahad Bin Sultan University, Tabuk 47721, Saudi ArabiaThe air quality in Jakarta, particularly concerning PM10 particulate matter, has become a serious concern due to its significant impact on health and the environment. The increase in pollution in this city is often triggered by industrial activities and seasonal factors, such as forest fires, which create challenges in air quality management. This study aims to map seasonal patterns in AQI PM10 concentrations and to develop a DL-based air quality prediction system that can operate in real-time, while also considering the impact of future forecasting timesteps on actual values. Additionally, this goal supports effective mitigation efforts and provides new insights for decision-making in addressing urban pollution. We propose an approach using GRU for time series forecasting, combined with an ARIMA model for imputing missing data. Utilizing air quality index data from Jakarta over more than 13 years, this research identifies consistent seasonal patterns, with PM10 concentrations peaking between May and October. Most days in this dataset fall into the “Moderate” category of the AQI, although there is considerable variation influenced by seasonal phenomena and industrial activities. Results show that the GRU model effectively captures fundamental patterns in the AQI PM10 data, although predictive accuracy tends to decrease as the forecasting interval increases. This study also provides new insights into the application of the GRU model for multi-timestep forecasting, focusing on short- and medium-term predictions that can be used as a decision-making tool for urban pollution mitigation.http://www.sciencedirect.com/science/article/pii/S2590123024016864Timestep ForecastingDeep LearningGated Recurrent UnitEnvironmentParticulate MatterAir Quality Index |
| spellingShingle | Furizal Alfian Ma'arif Iswanto Suwarno Alya Masitha Lathifatul Aulia Abdel-Nasser Sharkawy Real-Time Mechanism Based on Deep Learning Approaches for Analyzing the Impact of Future Timestep Forecasts on Actual Air Quality Index of PM10 Results in Engineering Timestep Forecasting Deep Learning Gated Recurrent Unit Environment Particulate Matter Air Quality Index |
| title | Real-Time Mechanism Based on Deep Learning Approaches for Analyzing the Impact of Future Timestep Forecasts on Actual Air Quality Index of PM10 |
| title_full | Real-Time Mechanism Based on Deep Learning Approaches for Analyzing the Impact of Future Timestep Forecasts on Actual Air Quality Index of PM10 |
| title_fullStr | Real-Time Mechanism Based on Deep Learning Approaches for Analyzing the Impact of Future Timestep Forecasts on Actual Air Quality Index of PM10 |
| title_full_unstemmed | Real-Time Mechanism Based on Deep Learning Approaches for Analyzing the Impact of Future Timestep Forecasts on Actual Air Quality Index of PM10 |
| title_short | Real-Time Mechanism Based on Deep Learning Approaches for Analyzing the Impact of Future Timestep Forecasts on Actual Air Quality Index of PM10 |
| title_sort | real time mechanism based on deep learning approaches for analyzing the impact of future timestep forecasts on actual air quality index of pm10 |
| topic | Timestep Forecasting Deep Learning Gated Recurrent Unit Environment Particulate Matter Air Quality Index |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024016864 |
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