Publication:
Prediction of missing data in rainfall dataset by using simple statistical method
Prediction of missing data in rainfall dataset by using simple statistical method
| cris.virtual.department | Universiti Malaysia Perlis | |
| cris.virtualsource.department | 06c2d2fe-cd6e-4763-82f9-c55423292bb5 | |
| dc.contributor.author | Izzati Amani Mohd Jafri | |
| dc.contributor.author | Norazian Mohamed Noor | |
| dc.contributor.author | Ahmad Zia Ul-Saufie | |
| dc.contributor.author | Annas Suwardi | |
| dc.date.accessioned | 2025-07-17T08:23:25Z | |
| dc.date.available | 2025-07-17T08:23:25Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Almost all of the data obtained from hydrological station contains missing data. Usually, this problem occurs due to equipment failures, maintenance work and human error. Incomplete dataset will reduce the ability of a statistical analysis and can cause a bias estimation due to systematic differences between observed and unobserved data. In this study, four simple statistical method such as Series Mean, Average Mean Top Bottom, Linear Interpolation and Nearest Neighbour were applied to predict the missing values in a rainfall dataset. An annual daily data for rainfall from nine selected monitoring station (from 2009 until 2018) were described using descriptive statistic. Then, the dataset were randomly simulated into 4 percentages of missing (5%, 10%, 15% and 20%) by using statistical package for social sciences software. The performance of this imputation methods were evaluated by using four performance indicators namely Mean Absolute Error, Root Mean Squared Error, Prediction Accuracy, and Index of Agreement. Overall, Linear Interpolation method was selected as the best imputation method to predict the missing data in the rainfall dataset. | |
| dc.identifier.doi | 10.1088/1755-1315/616/1/012005 | |
| dc.identifier.uri | https://iopscience.iop.org/article/10.1088/1755-1315/616/1/012005/pdf | |
| dc.identifier.uri | https://iopscience.iop.org/article/10.1088/1755-1315/616/1/012005 | |
| dc.identifier.uri | https://iopscience.iop.org/ | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14170/14141 | |
| dc.language.iso | en | |
| dc.publisher | IOP Publishing | |
| dc.relation.conference | 2nd International Conference on Green Environmental Engineering and Technology 2020 | |
| dc.relation.ispartof | IOP Conference Series: Earth and Environmental Science | |
| dc.relation.issn | 1755-1307 | |
| dc.relation.issn | 1755-1315 | |
| dc.title | Prediction of missing data in rainfall dataset by using simple statistical method | |
| dc.type | Resource Types::text::conference output::conference proceedings | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 9 | |
| oaire.citation.issue | 1 | |
| oaire.citation.startPage | 1 | |
| oaire.citation.volume | 616 | |
| oairecerif.author.affiliation | Universiti Malaysia Perlis | |
| oairecerif.author.affiliation | Universiti Malaysia Perlis | |
| oairecerif.author.affiliation | Universiti Malaysia Perlis | |
| oairecerif.author.affiliation | Universitas Negeri Makasar |
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