Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO
Air pollution is the entry or inclusion of living things, energy substances, and other components into the air. Moreover, Air pollution is the presence of one or several contaminants in the outside atmospheric air such as dust, foam, gas, fog, smoke or steam in large quantities with various properti...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8887156/ |
| _version_ | 1818735878516768768 |
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| author | Rezzy Eko Caraka Rung Ching Chen Toni Toharudin Bens Pardamean Hasbi Yasin Shih Hung Wu |
| author_facet | Rezzy Eko Caraka Rung Ching Chen Toni Toharudin Bens Pardamean Hasbi Yasin Shih Hung Wu |
| author_sort | Rezzy Eko Caraka |
| collection | DOAJ |
| description | Air pollution is the entry or inclusion of living things, energy substances, and other components into the air. Moreover, Air pollution is the presence of one or several contaminants in the outside atmospheric air such as dust, foam, gas, fog, smoke or steam in large quantities with various properties and time intervals of the contaminants in the air resulting in disturbances to the lives of humans, plants or animals. One of the parameters measured in determining air quality is PM<sub>2.5</sub>. However, PM<sub>2.5</sub> has a higher probability of being able to enter the lower respiratory tract because small particle diameters can potentially pass through the lower respiratory tract. In this paper, we will get two different insight. First, the probability of status change using Markov chain and second, forecasting by using VAR-NN-PSO. More details we classify by three classifications no risk (1-30), medium risk (30-48), and moderate (>49) in Pingtung and Chaozhou. This data is starting from January 2014 to May 2019 and it can be modeled with the Markov chain. At the same time, we perform Hybrid VAR-NN-PSO to forecast PM<sub>2.5</sub> in Pingtung and Chaozhou. In this optimization, the search for best solutions is carried out by a population consisting of several particles. Based on the results of the discussion, opportunities for the transition from monthly status change are obtained continuous stochastic time with a stationary probability distribution. Regarding the VAR-NN-PSO, we obtained the mean absolute percentage error (MAPE) 3.57% for PM<sub>2.5</sub> data in Pingtung and 4.87% for PM<sub>2.5</sub> data in Chaozhou, respectively. This model can be predicted to forecasting 180 days ahead. Besides, the population in PSO has generated randomly with the smallest value and the high value the accuracy. |
| first_indexed | 2024-12-18T00:28:15Z |
| format | Article |
| id | doaj.art-9229ef038b89443080656fbbf0872b14 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T00:28:15Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9229ef038b89443080656fbbf0872b142022-12-21T21:27:11ZengIEEEIEEE Access2169-35362019-01-01716165416166510.1109/ACCESS.2019.29504398887156Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSORezzy Eko Caraka0https://orcid.org/0000-0002-1812-7478Rung Ching Chen1https://orcid.org/0000-0001-7621-1988Toni Toharudin2Bens Pardamean3Hasbi Yasin4Shih Hung Wu5Department of Information Management, Chaoyang University of Technology, Taichung, TaiwanDepartment of Information Management, Chaoyang University of Technology, Taichung, TaiwanDepartment of Statistics, Padjadjaran University, Sumedang, IndonesiaBioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, IndonesiaDepartment of Statistics, Diponegoro University, Semarang, IndonesiaDepartment of Information Management, Chaoyang University of Technology, Taichung, TaiwanAir pollution is the entry or inclusion of living things, energy substances, and other components into the air. Moreover, Air pollution is the presence of one or several contaminants in the outside atmospheric air such as dust, foam, gas, fog, smoke or steam in large quantities with various properties and time intervals of the contaminants in the air resulting in disturbances to the lives of humans, plants or animals. One of the parameters measured in determining air quality is PM<sub>2.5</sub>. However, PM<sub>2.5</sub> has a higher probability of being able to enter the lower respiratory tract because small particle diameters can potentially pass through the lower respiratory tract. In this paper, we will get two different insight. First, the probability of status change using Markov chain and second, forecasting by using VAR-NN-PSO. More details we classify by three classifications no risk (1-30), medium risk (30-48), and moderate (>49) in Pingtung and Chaozhou. This data is starting from January 2014 to May 2019 and it can be modeled with the Markov chain. At the same time, we perform Hybrid VAR-NN-PSO to forecast PM<sub>2.5</sub> in Pingtung and Chaozhou. In this optimization, the search for best solutions is carried out by a population consisting of several particles. Based on the results of the discussion, opportunities for the transition from monthly status change are obtained continuous stochastic time with a stationary probability distribution. Regarding the VAR-NN-PSO, we obtained the mean absolute percentage error (MAPE) 3.57% for PM<sub>2.5</sub> data in Pingtung and 4.87% for PM<sub>2.5</sub> data in Chaozhou, respectively. This model can be predicted to forecasting 180 days ahead. Besides, the population in PSO has generated randomly with the smallest value and the high value the accuracy.https://ieeexplore.ieee.org/document/8887156/PM₂.₅Markov chainstochasticVARPSOneural network |
| spellingShingle | Rezzy Eko Caraka Rung Ching Chen Toni Toharudin Bens Pardamean Hasbi Yasin Shih Hung Wu Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO IEEE Access PM₂.₅ Markov chain stochastic VAR PSO neural network |
| title | Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO |
| title_full | Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO |
| title_fullStr | Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO |
| title_full_unstemmed | Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO |
| title_short | Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO |
| title_sort | prediction of status particulate matter 2 5 using state markov chain stochastic process and hybrid var nn pso |
| topic | PM₂.₅ Markov chain stochastic VAR PSO neural network |
| url | https://ieeexplore.ieee.org/document/8887156/ |
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