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-...
| Autori principali: | , , , |
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| Natura: | Articolo |
| Lingua: | English English |
| Pubblicazione: |
Korean Meteorological Society and Springer Nature
2020
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| Soggetti: | |
| Accesso online: | 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 |
Accesso online
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.pdfhttp://umpir.ump.edu.my/id/eprint/30291/7/The%20Effectiveness%20of%20a%20Probabilistic%20Principal%20Component%20Analysis.pdf