Identification of a probability distribution for extreme rainfall series in East Malaysia
The goal of this study was to evaluate the goodness-of-fit of the alternate probability distributions to sequences of the annual maximum stream flows in the East Malaysian states of Sabah and Sarawak. We will never know with certainty, the actual amount of rainfall that will occur in the futur...
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2004
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/7132/1/24p%20ISMAIL%20IBRAHIM.pdf http://eprints.uthm.edu.my/7132/2/ISMAIL%20IBRAHIM%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/7132/3/ISMAIL%20IBRAHIM%20WATERMARK.pdf http://eprints.uthm.edu.my/7132/ |
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| Summary: | The goal of this study was to evaluate the goodness-of-fit of the alternate
probability distributions to sequences of the annual maximum stream flows in the
East Malaysian states of Sabah and Sarawak. We will never know with certainty,
the actual amount of rainfall that will occur in the future. So a statistical analysis of
this nature can provide guidance on which probability distributions can give
reasonable approximation. Basically, this study is a statistical analysis on extreme
annual rainfall series in East Malaysia. It will discuss the comparative assessment
of eight candidate distributions in providing accurate and reliable maximum rainfall
estimates for East Malaysia. The models considered were the Exponential (EXP),
Gamma (GAM), Generalized Extreme Value (GEV), Generalized Logistic (GLO),
Generalized Pareto (GPA), Gumbel (GUM), Pearson Type III (PE3) and Wakeby
(WAK). Annual maximum rainfall series for one-hour resolution from a network of
ten Principal Gauging Stations located five each in Sabah and Sarawak were
selected for this study. On top of that, data for the fifteen-minutes were also taken
for analysis to act as a check to the result. The length of rainfall records varies from
seventeen to twenty-one years. Model parameters were estimated using the L�moment method. The quantitative assessment of the descriptive ability of each
model was based on using the Probability Plot Correlation Coefficient (PPCC) test
combined with Relative Root Mean Squared Error (RRMSE), Root Mean Squared
Error (RMSE) and Maximum Absolute Error (MAE). Ranking of PPCC in
descending order and the other three criteria on ascending orders were taken and the
top three distributions from the ranking for each station were chosen. The GEV
distribution came out on top that occurs frequently on most of the stations is
selected as the best fitting distribution to describe the extreme rainfall series for East
Malaysia. |
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