Che Mat Nor, Siti Mariana and Shaharudin, Shazlyn Milleana and Ismail, Shuhaida and Zainuddin, Nurul Hila and Tan, Mou Leong (2020) A comparative study of different imputation methods for daily rainfall data in east-coast Peninsular Malaysia. Bulletin of Electrical Engineering and Informatics, 9 (2). pp. 635-643. ISSN 2089-3191
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Rainfall data are the most significant values in hydrology and climatology modelling. However, the datasets are prone to missing values due to various issues. This study aspires to impute the rainfall missing values by using various imputation method such as Replace by Mean, Nearest Neighbor, Random Forest, Non-linear Interactive Partial Least-Square (NIPALS) and Markov Chain Monte Carlo (MCMC). Daily rainfall datasets from 48 rainfall stations across east-coast Peninsular Malaysia were used in this study. The dataset were then fed into Multiple Linear Regression (MLR) model. The performance of abovementioned methods were evaluated using Root Mean Square Method (RMSE), Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency Coefficient (CE). The experimental results showed that RF coupled with MLR (RF-MLR) approach was attained as more fitting for satisfying the missing data in east-coast Peninsular Malaysia.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | MCMC; Missing value; Nearest neighbor; NIPALS; Random forest; Replace by mean |
| Subjects: | Q Science > QC Physics > QC851-999 Meteorology. Climatology. Including the earth's atmosphere > QC980-999 Climatology and weather |
| Divisions: | Faculty of Applied Science and Technology > Department of Postgraduate |
| Depositing User: | Miss Afiqah Faiqah Mohd Hafiz |
| Date Deposited: | 26 Jan 2022 07:00 |
| Last Modified: | 26 Jan 2022 07:00 |
| URI: | http://eprints.uthm.edu.my/id/eprint/6100 |
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