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Development of missing data prediction model for carbon monoxide

Carbon monoxide (CO) is one of the most important pollutants since it is selected for API calculation. Therefore, it is paramount to ensure that there is no missing data of CO during the analysis. There are numbers of occurrences that may contribute to the missing data problems such as inability o...

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Main Authors: Abd Rani, Nurul Latiffah, Azid, Azman, Abdullah Sani, Muhamad Shirwan, Samsudin, Mohd Saiful, Ku Yusof, Ku Mohd Kalkausar, Muhammad Amin, Siti Noor Syuhada, Khalit, Saiful Iskandar
Format: Article
Language:English
English
Published: Penerbit UTM Press 2019
Subjects:
QA Mathematics
QA300 Analysis
QD Chemistry
Online Access:http://irep.iium.edu.my/70673/1/70673_Development%20of%20missing%20data%20prediction.pdf
http://irep.iium.edu.my/70673/2/70673_Development%20of%20missing%20data%20prediction_WOS.pdf
http://irep.iium.edu.my/70673/
https://mjfas.utm.my/index.php/mjfas/article/view/969/pdf
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spelling my.iium.irep.706732019-07-12T07:57:25Z http://irep.iium.edu.my/70673/ Development of missing data prediction model for carbon monoxide Abd Rani, Nurul Latiffah Azid, Azman Abdullah Sani, Muhamad Shirwan Samsudin, Mohd Saiful Ku Yusof, Ku Mohd Kalkausar Muhammad Amin, Siti Noor Syuhada Khalit, Saiful Iskandar QA Mathematics QA300 Analysis QD Chemistry Carbon monoxide (CO) is one of the most important pollutants since it is selected for API calculation. Therefore, it is paramount to ensure that there is no missing data of CO during the analysis. There are numbers of occurrences that may contribute to the missing data problems such as inability of the instrument to record certain parameters. In view of this fact, a CO prediction model needs to be developed to address this problem. A dataset of meteorological and air pollutants value was obtained from the Air Quality Division, Department of Environment Malaysia (DOE). A total of 113112 datasets were used to develop the model using sensitivity analysis (SA) through artificial neural network (ANN). SA showed particulate matter (PM10) and ozone (O3) were the most significant input variables for missing data prediction model of CO. Three hidden nodes were the optimum number to develop the ANN model with the value of R2 equal to 0.5311. Both models (artificial neural network-carbon monoxide-all parameters (ANN-CO-AP) and artificial neural network-carbon monoxide-leave out (ANN-CO-LO)) showed high value of R2 (0.7639 and 0.5311) and low value of RMSE (0.2482 and 0.3506), respectively. These values indicated that the models might only employ the most significant input variables to represent the CO rather than using all input variables. Penerbit UTM Press 2019-02 Article PeerReviewed application/pdf en http://irep.iium.edu.my/70673/1/70673_Development%20of%20missing%20data%20prediction.pdf application/pdf en http://irep.iium.edu.my/70673/2/70673_Development%20of%20missing%20data%20prediction_WOS.pdf Abd Rani, Nurul Latiffah and Azid, Azman and Abdullah Sani, Muhamad Shirwan and Samsudin, Mohd Saiful and Ku Yusof, Ku Mohd Kalkausar and Muhammad Amin, Siti Noor Syuhada and Khalit, Saiful Iskandar (2019) Development of missing data prediction model for carbon monoxide. Malaysian Journal of Fundamental and Applied Sciences (MJFAS), 15 (1 (Jan-Feb)). pp. 13-17. ISSN 2289-5981 https://mjfas.utm.my/index.php/mjfas/article/view/969/pdf
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
topic QA Mathematics
QA300 Analysis
QD Chemistry
spellingShingle QA Mathematics
QA300 Analysis
QD Chemistry
Abd Rani, Nurul Latiffah
Azid, Azman
Abdullah Sani, Muhamad Shirwan
Samsudin, Mohd Saiful
Ku Yusof, Ku Mohd Kalkausar
Muhammad Amin, Siti Noor Syuhada
Khalit, Saiful Iskandar
Development of missing data prediction model for carbon monoxide
description Carbon monoxide (CO) is one of the most important pollutants since it is selected for API calculation. Therefore, it is paramount to ensure that there is no missing data of CO during the analysis. There are numbers of occurrences that may contribute to the missing data problems such as inability of the instrument to record certain parameters. In view of this fact, a CO prediction model needs to be developed to address this problem. A dataset of meteorological and air pollutants value was obtained from the Air Quality Division, Department of Environment Malaysia (DOE). A total of 113112 datasets were used to develop the model using sensitivity analysis (SA) through artificial neural network (ANN). SA showed particulate matter (PM10) and ozone (O3) were the most significant input variables for missing data prediction model of CO. Three hidden nodes were the optimum number to develop the ANN model with the value of R2 equal to 0.5311. Both models (artificial neural network-carbon monoxide-all parameters (ANN-CO-AP) and artificial neural network-carbon monoxide-leave out (ANN-CO-LO)) showed high value of R2 (0.7639 and 0.5311) and low value of RMSE (0.2482 and 0.3506), respectively. These values indicated that the models might only employ the most significant input variables to represent the CO rather than using all input variables.
format Article
author Abd Rani, Nurul Latiffah
Azid, Azman
Abdullah Sani, Muhamad Shirwan
Samsudin, Mohd Saiful
Ku Yusof, Ku Mohd Kalkausar
Muhammad Amin, Siti Noor Syuhada
Khalit, Saiful Iskandar
author_facet Abd Rani, Nurul Latiffah
Azid, Azman
Abdullah Sani, Muhamad Shirwan
Samsudin, Mohd Saiful
Ku Yusof, Ku Mohd Kalkausar
Muhammad Amin, Siti Noor Syuhada
Khalit, Saiful Iskandar
author_sort Abd Rani, Nurul Latiffah
title Development of missing data prediction model for carbon monoxide
title_short Development of missing data prediction model for carbon monoxide
title_full Development of missing data prediction model for carbon monoxide
title_fullStr Development of missing data prediction model for carbon monoxide
title_full_unstemmed Development of missing data prediction model for carbon monoxide
title_sort development of missing data prediction model for carbon monoxide
publisher Penerbit UTM Press
publishDate 2019
url http://irep.iium.edu.my/70673/1/70673_Development%20of%20missing%20data%20prediction.pdf
http://irep.iium.edu.my/70673/2/70673_Development%20of%20missing%20data%20prediction_WOS.pdf
http://irep.iium.edu.my/70673/
https://mjfas.utm.my/index.php/mjfas/article/view/969/pdf
_version_ 1643619928659460096
score 13.211869

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