ENHANCING ECOSYSTEM BIODIVERSITY THROUGH AIR POLLUTION CONCENTRATIONS PREDICTION USING SUPPORT VECTOR REGRESSION APPROACHES

Air is the most crucial element for the survival of life on Earth. The air we breathe has a profound effect on our ecosystem biodiversity. Consequently, it is always prudent to monitor the air quality in our environment. There are few ways can be done in predicting the air pollution index (API) like...

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Published in:INTERNATIONAL JOURNAL OF CONSERVATION SCIENCE
Main Authors: Solehah, Syaidatul Umairah; Abidin, Aida Wati Zainan; Warris, Saiful Nizam; Shaziayani, Wan Nur; Osman, Balkish Mohd; Ibrahim, Nurain; Noor, Norazian Mohamed; Ul-saufie, Ahmad Zia
Format: Article
Language:English
Published: Romanian Inventors Forum 2023
Subjects:
Art
Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001128370800017
author Solehah
Syaidatul Umairah; Abidin
Aida Wati Zainan; Warris
Saiful Nizam; Shaziayani
Wan Nur; Osman
Balkish Mohd; Ibrahim
Nurain; Noor
Norazian Mohamed; Ul-saufie
Ahmad Zia
spellingShingle Solehah
Syaidatul Umairah; Abidin
Aida Wati Zainan; Warris
Saiful Nizam; Shaziayani
Wan Nur; Osman
Balkish Mohd; Ibrahim
Nurain; Noor
Norazian Mohamed; Ul-saufie
Ahmad Zia
ENHANCING ECOSYSTEM BIODIVERSITY THROUGH AIR POLLUTION CONCENTRATIONS PREDICTION USING SUPPORT VECTOR REGRESSION APPROACHES
Art
author_facet Solehah
Syaidatul Umairah; Abidin
Aida Wati Zainan; Warris
Saiful Nizam; Shaziayani
Wan Nur; Osman
Balkish Mohd; Ibrahim
Nurain; Noor
Norazian Mohamed; Ul-saufie
Ahmad Zia
author_sort Solehah
spelling Solehah, Syaidatul Umairah; Abidin, Aida Wati Zainan; Warris, Saiful Nizam; Shaziayani, Wan Nur; Osman, Balkish Mohd; Ibrahim, Nurain; Noor, Norazian Mohamed; Ul-saufie, Ahmad Zia
ENHANCING ECOSYSTEM BIODIVERSITY THROUGH AIR POLLUTION CONCENTRATIONS PREDICTION USING SUPPORT VECTOR REGRESSION APPROACHES
INTERNATIONAL JOURNAL OF CONSERVATION SCIENCE
English
Article
Air is the most crucial element for the survival of life on Earth. The air we breathe has a profound effect on our ecosystem biodiversity. Consequently, it is always prudent to monitor the air quality in our environment. There are few ways can be done in predicting the air pollution index (API) like data mining. Therefore, this study aimed to evaluate three types of support vector regression (linear, SVR, libSVR) in predicting the air pollutant concentration and identify the best model. This study also would like to calculate the API by using the proposed model. The secondary daily data is used in this study from year 2002 to 2020 from the Department of Environment (DoE) Malaysia which located at Petaling Jaya monitoring station. There are six major pollutants that have been focusing in this work like PM10, PM2.5, SO2, NO2, CO, and O3. The root means square error (RMSE), mean absolute error (MAE) and relative error (RE) were used to evaluate the performance of the regression models. Experimental results showed that the best model is linear SVR with average of RMSE = 5.548, MAE = 3.490, and RE = 27.98% because had the lowest total rank value of RMSE, MAE, and RE for five air pollutants (PM10, PM2.5, SO2, CO, O3) in this study. Unlikely for NO2, the best model is support vector regression (SVR) with RMSE = 0.007, MAE = 0.006, and RE = 20.75% in predicting the air pollutant concentration. This work also illustrates that combining data mining with air pollutants prediction is an efficient and convenient way to solve some related environment problems. The best model has the potential to be applied as an early warning system to inform local authorities about the air quality and can reliably predict the daily air pollution events over three consecutive days. Besides, good air quality plays a significant role in supporting biodiversity and maintaning healthy ecosystems.
Romanian Inventors Forum
2067-533X
2067-8223
2023
14
4
10.36868/IJCS.2023.04.24
Art
gold
WOS:001128370800017
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001128370800017
title ENHANCING ECOSYSTEM BIODIVERSITY THROUGH AIR POLLUTION CONCENTRATIONS PREDICTION USING SUPPORT VECTOR REGRESSION APPROACHES
title_short ENHANCING ECOSYSTEM BIODIVERSITY THROUGH AIR POLLUTION CONCENTRATIONS PREDICTION USING SUPPORT VECTOR REGRESSION APPROACHES
title_full ENHANCING ECOSYSTEM BIODIVERSITY THROUGH AIR POLLUTION CONCENTRATIONS PREDICTION USING SUPPORT VECTOR REGRESSION APPROACHES
title_fullStr ENHANCING ECOSYSTEM BIODIVERSITY THROUGH AIR POLLUTION CONCENTRATIONS PREDICTION USING SUPPORT VECTOR REGRESSION APPROACHES
title_full_unstemmed ENHANCING ECOSYSTEM BIODIVERSITY THROUGH AIR POLLUTION CONCENTRATIONS PREDICTION USING SUPPORT VECTOR REGRESSION APPROACHES
title_sort ENHANCING ECOSYSTEM BIODIVERSITY THROUGH AIR POLLUTION CONCENTRATIONS PREDICTION USING SUPPORT VECTOR REGRESSION APPROACHES
container_title INTERNATIONAL JOURNAL OF CONSERVATION SCIENCE
language English
format Article
description Air is the most crucial element for the survival of life on Earth. The air we breathe has a profound effect on our ecosystem biodiversity. Consequently, it is always prudent to monitor the air quality in our environment. There are few ways can be done in predicting the air pollution index (API) like data mining. Therefore, this study aimed to evaluate three types of support vector regression (linear, SVR, libSVR) in predicting the air pollutant concentration and identify the best model. This study also would like to calculate the API by using the proposed model. The secondary daily data is used in this study from year 2002 to 2020 from the Department of Environment (DoE) Malaysia which located at Petaling Jaya monitoring station. There are six major pollutants that have been focusing in this work like PM10, PM2.5, SO2, NO2, CO, and O3. The root means square error (RMSE), mean absolute error (MAE) and relative error (RE) were used to evaluate the performance of the regression models. Experimental results showed that the best model is linear SVR with average of RMSE = 5.548, MAE = 3.490, and RE = 27.98% because had the lowest total rank value of RMSE, MAE, and RE for five air pollutants (PM10, PM2.5, SO2, CO, O3) in this study. Unlikely for NO2, the best model is support vector regression (SVR) with RMSE = 0.007, MAE = 0.006, and RE = 20.75% in predicting the air pollutant concentration. This work also illustrates that combining data mining with air pollutants prediction is an efficient and convenient way to solve some related environment problems. The best model has the potential to be applied as an early warning system to inform local authorities about the air quality and can reliably predict the daily air pollution events over three consecutive days. Besides, good air quality plays a significant role in supporting biodiversity and maintaning healthy ecosystems.
publisher Romanian Inventors Forum
issn 2067-533X
2067-8223
publishDate 2023
container_volume 14
container_issue 4
doi_str_mv 10.36868/IJCS.2023.04.24
topic Art
topic_facet Art
accesstype gold
id WOS:001128370800017
url https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001128370800017
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