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Comparison of missing rainfall data treatment analysis at Kenyir Lake

Rainfall is one of the frequent data used in weather-related studies. Sometimes the data have missing information that needs the treatment to make sure the data can be useful, complete and reliable. There are many methods in treating missing data suggested by previous studies. The best selected meth...

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Main Authors: Azreen Harina, Azman, Nurul Nadrah Aqilah, Tukimat, M A, Male
Format: Article
Language:English
Published: IOP Publishing 2021
Subjects:
TA Engineering (General). Civil engineering (General)
TD Environmental technology. Sanitary engineering
Online Access:http://umpir.ump.edu.my/id/eprint/31460/1/iscee2020.pdf
http://umpir.ump.edu.my/id/eprint/31460/
https://iopscience.iop.org/article
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spelling my.ump.umpir.314602021-05-24T06:05:45Z http://umpir.ump.edu.my/id/eprint/31460/ Comparison of missing rainfall data treatment analysis at Kenyir Lake Azreen Harina, Azman Nurul Nadrah Aqilah, Tukimat M A, Male TA Engineering (General). Civil engineering (General) TD Environmental technology. Sanitary engineering Rainfall is one of the frequent data used in weather-related studies. Sometimes the data have missing information that needs the treatment to make sure the data can be useful, complete and reliable. There are many methods in treating missing data suggested by previous studies. The best selected method to estimate missing rainfall data in different regions may vary depending on the rainfall pattern and spatial distribution. Therefore, this paper discussed and compared 3 different methods in missing data treatment. The selected methods are Expectation Maximization (EM), Inverse Distance Weighted (IDW) and Multiple Imputation (MI). After analysis, the best method is IDW based on root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (r) and percentage of error (% of error) values. The IDW method has RMSE, MAE values and the lowest % of error values. In addition, the r value of IDW method is highest compared to EM and MI method. MI method recorded the highest values of RMSE, MAE and % of error with the lowest r value that proved MI method is the least accurate method to use in missing data treatment. After all methods were implemented, it proved that the IDW method is the best way to treat missing data because the analysis shows monthly rainfall distribution for 4 treatment stations in line to 3 missing data stations compared to EM and MI methods. IOP Publishing 2021 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/31460/1/iscee2020.pdf Azreen Harina, Azman and Nurul Nadrah Aqilah, Tukimat and M A, Male (2021) Comparison of missing rainfall data treatment analysis at Kenyir Lake. IOP Conference Series: Materials Science and Engineering, 1144. pp. 1-10. ISSN 1757-8981 (Print), 1757-899X (Online) https://iopscience.iop.org/article doi:10.1088/1757-899X/1144/1/012046
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TA Engineering (General). Civil engineering (General)
TD Environmental technology. Sanitary engineering
spellingShingle TA Engineering (General). Civil engineering (General)
TD Environmental technology. Sanitary engineering
Azreen Harina, Azman
Nurul Nadrah Aqilah, Tukimat
M A, Male
Comparison of missing rainfall data treatment analysis at Kenyir Lake
description Rainfall is one of the frequent data used in weather-related studies. Sometimes the data have missing information that needs the treatment to make sure the data can be useful, complete and reliable. There are many methods in treating missing data suggested by previous studies. The best selected method to estimate missing rainfall data in different regions may vary depending on the rainfall pattern and spatial distribution. Therefore, this paper discussed and compared 3 different methods in missing data treatment. The selected methods are Expectation Maximization (EM), Inverse Distance Weighted (IDW) and Multiple Imputation (MI). After analysis, the best method is IDW based on root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (r) and percentage of error (% of error) values. The IDW method has RMSE, MAE values and the lowest % of error values. In addition, the r value of IDW method is highest compared to EM and MI method. MI method recorded the highest values of RMSE, MAE and % of error with the lowest r value that proved MI method is the least accurate method to use in missing data treatment. After all methods were implemented, it proved that the IDW method is the best way to treat missing data because the analysis shows monthly rainfall distribution for 4 treatment stations in line to 3 missing data stations compared to EM and MI methods.
format Article
author Azreen Harina, Azman
Nurul Nadrah Aqilah, Tukimat
M A, Male
author_facet Azreen Harina, Azman
Nurul Nadrah Aqilah, Tukimat
M A, Male
author_sort Azreen Harina, Azman
title Comparison of missing rainfall data treatment analysis at Kenyir Lake
title_short Comparison of missing rainfall data treatment analysis at Kenyir Lake
title_full Comparison of missing rainfall data treatment analysis at Kenyir Lake
title_fullStr Comparison of missing rainfall data treatment analysis at Kenyir Lake
title_full_unstemmed Comparison of missing rainfall data treatment analysis at Kenyir Lake
title_sort comparison of missing rainfall data treatment analysis at kenyir lake
publisher IOP Publishing
publishDate 2021
url http://umpir.ump.edu.my/id/eprint/31460/1/iscee2020.pdf
http://umpir.ump.edu.my/id/eprint/31460/
https://iopscience.iop.org/article
_version_ 1701163155070124032
score 13.211869

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