Prediction of PM10 pollution using principal component regression and hybrid artificial neural network model
Air pollution, especially particulate matter (PM) pollution, has a significant impact on India. PM pollution is due to roadside dust, fossil fuel use, vehicular population, and industrial emissions. PM10 forecasting model development is essential because it permits the experts and the citizens to...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Prince of Songkla University
2022-10-01
|
| Series: | Songklanakarin Journal of Science and Technology (SJST) |
| Subjects: | |
| Online Access: | https://sjst.psu.ac.th/journal/44-5/13.pdf |
| _version_ | 1827960351312838656 |
|---|---|
| author | Sateesh N Hosamane |
| author_facet | Sateesh N Hosamane |
| author_sort | Sateesh N Hosamane |
| collection | DOAJ |
| description | Air pollution, especially particulate matter (PM) pollution, has a significant impact on India. PM pollution is due to
roadside dust, fossil fuel use, vehicular population, and industrial emissions. PM10 forecasting model development is essential
because it permits the experts and the citizens to take appropriate actions to restrict their exposure and execute protective
measures to improve air quality. This study aimed to develop a specialized computational intelligence methodology that uses
principal component (PC) based artificial neural networks (ANN). The model was used to forecast PM10 in ambient air using
meteorological data. This application is demonstrated for monitoring data from the urban area of Belagavi city of Karnataka
state, India. Principal component analysis (PCA) was applied to understand the interactions between PM10 concentration and
meteorological data. The analysis found that the PCANN model is better than the principal component regression (PCR) model,
based on using various performance indexes (MAE, MSE, MAPE, RMSE, R, and R2). The PM10 predictive model performance
was satisfactory, with a MAPE of 0.069. The overall predictive capability of PM10 was 89.59% in terms of R. |
| first_indexed | 2024-04-09T16:07:06Z |
| format | Article |
| id | doaj.art-1c3df43b1f1e4c34a5b8c8c470727d46 |
| institution | Directory Open Access Journal |
| issn | 0125-3395 |
| language | English |
| last_indexed | 2024-04-09T16:07:06Z |
| publishDate | 2022-10-01 |
| publisher | Prince of Songkla University |
| record_format | Article |
| series | Songklanakarin Journal of Science and Technology (SJST) |
| spelling | doaj.art-1c3df43b1f1e4c34a5b8c8c470727d462023-04-25T04:10:51ZengPrince of Songkla UniversitySongklanakarin Journal of Science and Technology (SJST)0125-33952022-10-014451256126310.14456/sjst-psu.2022.163Prediction of PM10 pollution using principal component regression and hybrid artificial neural network modelSateesh N Hosamane0Department of Chemical Engineering, KLE Dr. M. S. Sheshgiri College of Engineering and Technology, Udyambag, Belgaum, 590008 IndiaAir pollution, especially particulate matter (PM) pollution, has a significant impact on India. PM pollution is due to roadside dust, fossil fuel use, vehicular population, and industrial emissions. PM10 forecasting model development is essential because it permits the experts and the citizens to take appropriate actions to restrict their exposure and execute protective measures to improve air quality. This study aimed to develop a specialized computational intelligence methodology that uses principal component (PC) based artificial neural networks (ANN). The model was used to forecast PM10 in ambient air using meteorological data. This application is demonstrated for monitoring data from the urban area of Belagavi city of Karnataka state, India. Principal component analysis (PCA) was applied to understand the interactions between PM10 concentration and meteorological data. The analysis found that the PCANN model is better than the principal component regression (PCR) model, based on using various performance indexes (MAE, MSE, MAPE, RMSE, R, and R2). The PM10 predictive model performance was satisfactory, with a MAPE of 0.069. The overall predictive capability of PM10 was 89.59% in terms of R.https://sjst.psu.ac.th/journal/44-5/13.pdfair pollutionpm10principal componentneural networkprediction |
| spellingShingle | Sateesh N Hosamane Prediction of PM10 pollution using principal component regression and hybrid artificial neural network model Songklanakarin Journal of Science and Technology (SJST) air pollution pm10 principal component neural network prediction |
| title | Prediction of PM10 pollution using principal component regression and hybrid artificial neural network model |
| title_full | Prediction of PM10 pollution using principal component regression and hybrid artificial neural network model |
| title_fullStr | Prediction of PM10 pollution using principal component regression and hybrid artificial neural network model |
| title_full_unstemmed | Prediction of PM10 pollution using principal component regression and hybrid artificial neural network model |
| title_short | Prediction of PM10 pollution using principal component regression and hybrid artificial neural network model |
| title_sort | prediction of pm10 pollution using principal component regression and hybrid artificial neural network model |
| topic | air pollution pm10 principal component neural network prediction |
| url | https://sjst.psu.ac.th/journal/44-5/13.pdf |
| work_keys_str_mv | AT sateeshnhosamane predictionofpm10pollutionusingprincipalcomponentregressionandhybridartificialneuralnetworkmodel |