Please use this identifier to cite or link to this item:
Title: Extreme rainfall analysis using different statistical models
Authors: Lee, Eugene Xun Wei
Keywords: Engineering::Civil engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Lee, E. X. W. (2022). Extreme rainfall analysis using different statistical models. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: WR-16
Abstract: It is important to design flood prevention systems such that they can strike a good balance between being able to handle a high enough rainfall, and not being too expensive and over-designed. To know what level of rainfall to design for, statistical analysis of historical rainfall values is done to make estimations on future rainfall amounts. The first step of analysing rainfall data is to select the most appropriate probability distribution curve. This will provide a better estimation of the return period of an extreme rainfall event. This study aims to identify the rainfall patterns in different regions of Singapore, by fitting daily rainfall data to different probability distributions and comparing their correlation coefficients and root mean square errors. In addition, the temperature patterns were also determined using the same method with maximum daily temperature. Yearly maximum daily rainfall and maximum temperature from three climate stations in Singapore over the last 40 years were used in this report, and MATLAB was used to perform data analysis. It was found that the Generalised Extreme Value distribution was the most appropriate distribution curve in general, with the Log Pearson Type III distribution showing similar results.
Schools: School of Civil and Environmental Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:CEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
Extreme Rainfall Analysis Using Different Statistical Models.pdf
  Restricted Access
3.49 MBAdobe PDFView/Open

Page view(s)

Updated on Jun 8, 2023

Download(s) 50

Updated on Jun 8, 2023

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.