The effectiveness of a probabilistic principal component analysis model and expectation maximisation algorithm in treating missing daily rainfall data
The reliability and accuracy of a risk assessment of extreme hydro-meteorological events are highly dependent on the quality of the historical rainfall time series data. However, missing data in a time series such as this could result in lower quality data. Therefore, this paper proposes a multiple-...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Korean Meteorological Society and Springer Nature
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/30291/1/The%20Effectiveness%20of%20a%20Probabilistic%20Principal%20Component%20Analysis%20Model%20and%20Expectation%20Maximisation%20Algorithm%20in%20Treating%20Missing%20Daily%20Rainfall%20Data.pdf http://umpir.ump.edu.my/id/eprint/30291/7/The%20Effectiveness%20of%20a%20Probabilistic%20Principal%20Component%20Analysis.pdf |
| Summary: | The reliability and accuracy of a risk assessment of extreme hydro-meteorological events are highly dependent on the quality of the historical rainfall time series data. However, missing data in a time series such as this could result in lower quality data. Therefore, this paper proposes a multiple-imputation algorithm for treating missing data without requiring information from adjoining monitoring stations. The proposed imputation algorithms are based on the M-component probabilistic principal component analysis model and an expectation maximisation algorithm (MPPCA-EM). In order to evaluate the effectiveness of
the MPPCA-EM imputation algorithm, six distinct historical daily rainfall time series data were recorded from six monitoring stations. These stations were located at the coastal and inland regions of the East-Coast Economic Region (ECER) Malaysia. The results of analysis show that, when it comes to treating missing historical daily rainfall time series data recorded from coastal monitoring stations, the 2-component probabilistic principal component analysis model and expectation-maximisation algorithm (2PPCA-EM) were found to be superior to the single- and multiple-imputation algorithms proposed in previous studies. On the contrary, the single-imputation algorithms as proposed in previous studies were superior to the MPPCA-EM imputation algorithms when treating missing historical daily rainfall time series data recorded from inland monitoring stations. |
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