Probability distributions comparative analysis in assessing rainfall process in time and space
The need for a reliable rainfall model to produce accurate simulation of rainfall series is imperative in water resources planning. Simulated series are used when there are shortages of observed series at location of interest. This study focuses on modelling of rainfall series with a range of pro...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
IAEME Publication
2017
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/63000/ http://irep.iium.edu.my/63000/ http://irep.iium.edu.my/63000/1/63000_Probability%20distributions%20comparative%20analysis_article.pdf http://irep.iium.edu.my/63000/2/63000_Probability%20distributions%20comparative%20analysis_scopus.pdf |
| Summary: | The need for a reliable rainfall model to produce accurate simulation of rainfall series
is imperative in water resources planning. Simulated series are used when there are
shortages of observed series at location of interest. This study focuses on modelling of
rainfall series with a range of probability distributions representing rainfall intensity of
the Space-Time Neyman Scott (ST-NS) model. Theoretically, the ST-NS model is
constructed by having parameters to represent the physical attributes of rainfall process.
Therefore having appropriate distributions to describe the parameters are critical so that
credible rainfall series could be generated. In this study, the performance of four
probability distributions namely Mixed-Exponential, Gamma, Weibull and Generalized
Pareto in representing rainfall intensity are assessed and compared. Model construction
of the ST-NS model involved the merging of rainfall data from sixteen stations located all
over Peninsular Malaysia. Simulations of hourly rainfall series for each distribution are
carried at out of sample site. Performance assessments between the distributions are
conducted using Root Mean Square Error, Akaike Information Criterion, Bayesian
Information Criterion, Kolmogrov-Smirnov Test and Anderson-Darling Test. Results
revealed that mixture type distributions tend to perform better. The performance of both
Mixed-Exponential and Generalized Pareto are very similar and both are equally good at
representing rain intensity in Peninsular Malaysia. The adopted method and the results
could also be extended to other tropical regions. |
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