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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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 |
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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 |
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