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The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

The comprehensives of particulate matter studies are needed in predicting future haze occurrences in Malaysia. This paper presents the application of Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) coupled with sensitivity analysis (SA) in order to recognize the pollutant rela...

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Main Authors:	Ku Yusof, Ku Mohd Kalkausar, Azid, Azman, Abdullah Sani, Muhamad Shirwan, Samsudin, Mohd Saiful, Muhammad Amin, Siti Noor Syuhada, Abd Rani, Nurul Latiffah, Jamalani, Mohd Asrul
Format:	Article
Language:	English English English
Published:	Penerbit UTM Press 2019
Subjects:	QD Chemistry
Online Access:	http://irep.iium.edu.my/71660/9/71660_The%20evaluation%20on%20artificial%20neural%20networks%20%28ANN%29_mycite.pdf http://irep.iium.edu.my/71660/15/71660_The%20evaluation%20on%20artificial%20neural%20networks%20%28ANN%29%20and%20multiple%20linear%20regressions%20%28MLR%29%20models%20over%20particulate%20matter%20%28PM10%29%20variability%20during%20haze%20and%20non-haze%20episodes_WOS.pdf http://irep.iium.edu.my/71660/21/71660_The%20evaluation%20on%20artificial%20neural%20networks%20%28ANN%29.pdf http://irep.iium.edu.my/71660/ https://mjfas.utm.my/index.php/mjfas/article/view/1004/pdf
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spelling	my.iium.irep.716602020-06-17T02:54:52Z http://irep.iium.edu.my/71660/ The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study Ku Yusof, Ku Mohd Kalkausar Azid, Azman Abdullah Sani, Muhamad Shirwan Samsudin, Mohd Saiful Muhammad Amin, Siti Noor Syuhada Abd Rani, Nurul Latiffah Jamalani, Mohd Asrul QD Chemistry The comprehensives of particulate matter studies are needed in predicting future haze occurrences in Malaysia. This paper presents the application of Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) coupled with sensitivity analysis (SA) in order to recognize the pollutant relationship status over particulate matter (PM10) in eastern region. Eight monitoring studies were used, involving 14 input parameters as independent variables including meteorological factors. In order to investigate the efficiency of ANN and MLR performance, two different weather circumstances were selected; haze and non-haze. The performance evaluation was characterized into two steps. Firstly, two models were developed based on ANN and MLR which denoted as full model, with all parameters (14 variables) were used as the input. SA was used as additional feature to rank the most contributed parameter to PM10 variations in both situations. Next, the model development was evaluated based on selected model, where only significant variables were selected as input. Three mathematical indices were introduced (R2, RMSE and SSE) to compare on both techniques. From the findings, ANN performed better in full and selected model, with both models were completely showed a significant result during hazy and non-hazy. On top of that, UVb and carbon monoxide were both variables that mutually predicted by ANN and MLR during hazy and non- hazy days, respectively. The precise predictions were required in helping any related agency to emphasize on pollutant that essentially contributed to PM10 variations, especially during haze period. Penerbit UTM Press 2019-03 Article PeerReviewed application/pdf en http://irep.iium.edu.my/71660/9/71660_The%20evaluation%20on%20artificial%20neural%20networks%20%28ANN%29_mycite.pdf application/pdf en http://irep.iium.edu.my/71660/15/71660_The%20evaluation%20on%20artificial%20neural%20networks%20%28ANN%29%20and%20multiple%20linear%20regressions%20%28MLR%29%20models%20over%20particulate%20matter%20%28PM10%29%20variability%20during%20haze%20and%20non-haze%20episodes_WOS.pdf application/pdf en http://irep.iium.edu.my/71660/21/71660_The%20evaluation%20on%20artificial%20neural%20networks%20%28ANN%29.pdf Ku Yusof, Ku Mohd Kalkausar and Azid, Azman and Abdullah Sani, Muhamad Shirwan and Samsudin, Mohd Saiful and Muhammad Amin, Siti Noor Syuhada and Abd Rani, Nurul Latiffah and Jamalani, Mohd Asrul (2019) The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study. Malaysian Journal of Fundamental and Applied Sciences, 15 (2 (March-April)). pp. 164-172. ISSN 2289-5981 E-ISSN 2289-599X https://mjfas.utm.my/index.php/mjfas/article/view/1004/pdf 10.11113/mjfas.v15n2.1004
institution	Universiti Islam Antarabangsa Malaysia
building	IIUM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	International Islamic University Malaysia
content_source	IIUM Repository (IREP)
url_provider	http://irep.iium.edu.my/
language	English English English
topic	QD Chemistry
spellingShingle	QD Chemistry Ku Yusof, Ku Mohd Kalkausar Azid, Azman Abdullah Sani, Muhamad Shirwan Samsudin, Mohd Saiful Muhammad Amin, Siti Noor Syuhada Abd Rani, Nurul Latiffah Jamalani, Mohd Asrul The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study
description	The comprehensives of particulate matter studies are needed in predicting future haze occurrences in Malaysia. This paper presents the application of Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) coupled with sensitivity analysis (SA) in order to recognize the pollutant relationship status over particulate matter (PM10) in eastern region. Eight monitoring studies were used, involving 14 input parameters as independent variables including meteorological factors. In order to investigate the efficiency of ANN and MLR performance, two different weather circumstances were selected; haze and non-haze. The performance evaluation was characterized into two steps. Firstly, two models were developed based on ANN and MLR which denoted as full model, with all parameters (14 variables) were used as the input. SA was used as additional feature to rank the most contributed parameter to PM10 variations in both situations. Next, the model development was evaluated based on selected model, where only significant variables were selected as input. Three mathematical indices were introduced (R2, RMSE and SSE) to compare on both techniques. From the findings, ANN performed better in full and selected model, with both models were completely showed a significant result during hazy and non-hazy. On top of that, UVb and carbon monoxide were both variables that mutually predicted by ANN and MLR during hazy and non- hazy days, respectively. The precise predictions were required in helping any related agency to emphasize on pollutant that essentially contributed to PM10 variations, especially during haze period.
format	Article
author	Ku Yusof, Ku Mohd Kalkausar Azid, Azman Abdullah Sani, Muhamad Shirwan Samsudin, Mohd Saiful Muhammad Amin, Siti Noor Syuhada Abd Rani, Nurul Latiffah Jamalani, Mohd Asrul
author_facet	Ku Yusof, Ku Mohd Kalkausar Azid, Azman Abdullah Sani, Muhamad Shirwan Samsudin, Mohd Saiful Muhammad Amin, Siti Noor Syuhada Abd Rani, Nurul Latiffah Jamalani, Mohd Asrul
author_sort	Ku Yusof, Ku Mohd Kalkausar
title	The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study
title_short	The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study
title_full	The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study
title_fullStr	The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study
title_full_unstemmed	The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study
title_sort	evaluation on artificial neural networks (ann) and multiple linear regressions (mlr) models over particulate matter (pm10) variability during haze and non-haze episodes: a decade case study
publisher	Penerbit UTM Press
publishDate	2019
url	http://irep.iium.edu.my/71660/9/71660_The%20evaluation%20on%20artificial%20neural%20networks%20%28ANN%29_mycite.pdf http://irep.iium.edu.my/71660/15/71660_The%20evaluation%20on%20artificial%20neural%20networks%20%28ANN%29%20and%20multiple%20linear%20regressions%20%28MLR%29%20models%20over%20particulate%20matter%20%28PM10%29%20variability%20during%20haze%20and%20non-haze%20episodes_WOS.pdf http://irep.iium.edu.my/71660/21/71660_The%20evaluation%20on%20artificial%20neural%20networks%20%28ANN%29.pdf http://irep.iium.edu.my/71660/ https://mjfas.utm.my/index.php/mjfas/article/view/1004/pdf
_version_	1672610124628230144
score	13.211869

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

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