Phua, Lih Jang
(2022)
*Analysis of Extreme Rainfall Events in Malaysia.*
Final Year Project (Bachelor), Tunku Abdul Rahman University College.

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## Abstract

Extreme rainfall is one of the studies that researchers are interested in until now. Extreme rainfall can be caused by the monsoon seasons. Northeast monsoon is the wet season where extreme rainfall usually happened. As consequence, flood strikes and then damage the economy and society in Malaysia. To find the pattern of the extreme rainfall, researchers have fitted various distributions using the extreme rainfall value that filtered using block maxima or peak-over-threshold. Five common distributions (exponential, gamma, generalised extreme value (GEV), generalised Pareto (GPE) and Weibull) are fitted in this study and goodness-of-fit test (Kolmogorov-Smirnov test and Anderson-Darling test) is conducted to evaluate. Besides fitting the extreme rainfall into distribution, the researcher wishes to identify the month where the extreme rainfall occurs. Quantile regression is one of the methods as it can choose the quantile and robust to the outliers. The extreme rainfall is then analysed using quantile regression with 0.75, 0.90 and 0.95 quantile to find the month where the extreme rainfall occurs. Box and whisker plot is used to identify the extreme rainfall. The logistic regression is used to find the probability of extreme rainfall occurrence for each month. As result, the GPE distribution fit the extreme rainfall value the best for Kelantan followed by the GEV distribution. The quantile regression also predicted the extreme rainfall month is in December, which the northeast monsoon strikes Kelantan. The actual monthly rainfall in December exceeds the 75th quantile for all the rainfall stations and 9 over 12 of the rainfall stations exceeds the 95th quantile. The logistic regression also shows that December has the highest probability of which extreme rainfall will occur. This concludes that the probability distribution for the extreme rainfall in Kelantan follows GPE distribution and quantile regression can predict the extreme rainfall event.

Item Type: | Final Year Project |
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Subjects: | Science > Mathematics Geography. Anthropology. Recreation > Manners and customs > Special events |

Faculties: | Faculty of Computing and Information Technology > Bachelor of Science (Honours) in Management Mathematics with Computing |

Depositing User: | Library Staff |

Date Deposited: | 17 Aug 2022 03:22 |

Last Modified: | 17 Aug 2022 03:22 |

URI: | https://eprints.tarc.edu.my/id/eprint/22485 |