Fitting statistical distribution of extreme rainfall data for the purpose of simulation

Shaharudin, Shazlyn Milleana; Ahmad, Norhaiza; Mohamed, Nur Syarafina; Mahdin, Hairulnizam

doi:10.11591/ijeecs.v18.i3.pp1367-1374

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…Analyzing the frequency of extreme rainfall events, a commonly used statistical approach involves employing probability distribution models [8,9]. As extreme rainfall series generally follow right-skewed distribution curves, the normal distribution, generalized extreme value distribution or generalized Pareto distribution [10][11][12][13][14]. Estimating return levels is one of the most important aspects of extreme value theory.…”

Section: Introductionmentioning

confidence: 99%

Changing of return periods of extreme rainfall using satellite observation in Java Island

Giarno,

Ruslana,

Rachmawardhani

et al. 2023

E3S Web Conf.

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As a flood-prone area and a centre of activity in Indonesia, the high intensity of rainfall needs to be watched out for. Excessive rainfall can lead to disastrous outcomes like floods and landslides. Analyzing the frequency of extreme rainfall events, calculated by a statistical approach involving employing probability distribution models and generalized extreme value distributions. Return levels are defined as the maximum value that will be exceeded once in a given time period. This study aims to map changes in maximum rainfall that have occurred repeatedly on the island of Java in the last 40 years. Changes in the rainfall return period are detected using CHIRPS rainfall estimates, by calculating every 20 years. The calculation results show high rainfall intensity in the south of Java Island for all return periods and there is an increase in rainfall in the north of Jakarta. The southern region of West Java and Central Java has also experienced a relatively high increase in rainfall intensity, especially over a 20-year period. Meanwhile in East Java Province, increased rainfall occurred in the easternmost part of the province.

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Section: Introductionmentioning

confidence: 99%

Changing of return periods of extreme rainfall using satellite observation in Java Island

Giarno,

Ruslana,

Rachmawardhani

et al. 2023

E3S Web Conf.

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“…According to their findings, the GEVD model was deemed the most suitable for characterizing Malaysia's yearly precipitation data. In a research conducted by Shaharudin et al (2020), they used several probability distributions, such as GPD, Lognormal, and Gamma to model the daily rainfall data in Malaysia. The study discovered that the GPD was appropriate for describing daily rainfall data.…”

Section: Introductionmentioning

confidence: 99%

Modeling Extreme Rainfall Events in the Tropical Monsoon Region: A Generalized Pareto Distribution Approach

Muzakir¹,

Sunusi²,

Thamrin³

2023

IJRP

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Extreme weather events have become more frequent due to climate change, and extreme rainfall is a common occurrence in the tropical monsoon areas. Makassar City was chosen as a representative area of the tropical monsoon climate. The objective of this research is to utilize the generalized Pareto distribution (GPD), along with its nested model (exponential), to predict the monthly return levels of extreme rainfall in the designated geographical region. The study utilized daily precipitation data from January 1980 to December 2022. To determine the monthly daily rainfall data that exceeds a certain threshold, the peaks over threshold (POT) method was applied. The result discovered that the exponential distribution is the most appropriate for extreme rainfall series in most months, except for February and July, where the GPD is more appropriate. No trends or seasonal patterns were identified in any of the months. The calculated return levels of extreme rainfall for each month at the 2, 3, 5, and 10-year return periods indicate that February has the highest rainfall return level for all selected return periods compared to other months with December and January following closely behind. These findings are expected to assist the government in developing flood prevention strategies and mitigating their effects, particularly during the rainy season's peak months in the city, which are December to February.

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“…9, No. 4, December 2020: 655 -661 656 annually [2][3][4][5]. PCA is sensitive to the outliers since it measures the variablility through the significance of the variance based on the computation of the eigenvalues as well as the eigenvectors of the covariance or correlation matrix in the dataset.…”

Section: Introductionmentioning

confidence: 99%

A modified correlation in principal component analysis for torrential rainfall patterns identification

Shaharudin

Ahmad

Nor

2020

IJ-AI

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This paper presents a modified correlation in principal component analysis (PCA) for selection number of clusters in identifying rainfall patterns. The approach of a clustering as guided by PCA is extensively employed in data with high dimension especially in identifying the spatial distribution patterns of daily torrential rainfall. Typically, a common method of identifying rainfall patterns for climatological investigation employed T mode-based Pearson correlation matrix to extract the relative variance retained. However, the data of rainfall in Peninsular Malaysia involved skewed observations in the direction of higher values with pure tendencies of values that are positive. Therefore, using Pearson correlation which was basing on PCA on rainfall set of data has the potentioal to influence the partitions of cluster as well as producing exceptionally clusters that are eneven in a space with high dimension. For current research, to resolve the unbalanced clusters challenge regarding the patterns of rainfall caused by the skewed character of the data, a robust dimension reduction method in PCA was employed. Thus, it led to the introduction of a robust measure in PCA with Tukey’s biweight correlation to downweigh observations along with the optimal breakdown point to obtain PCA’s quantity of components. Outcomes of this study displayed a highly substantial progress for the robust PCA, contrasting with the PCA-based Pearson correlation in respects to the average amount of acquired clusters and indicated 70% variance cumulative percentage at the breakdown point of 0.4.

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Fitting statistical distribution of extreme rainfall data for the purpose of simulation

Cited by 3 publications

References 10 publications

Changing of return periods of extreme rainfall using satellite observation in Java Island

Changing of return periods of extreme rainfall using satellite observation in Java Island

Modeling Extreme Rainfall Events in the Tropical Monsoon Region: A Generalized Pareto Distribution Approach

A modified correlation in principal component analysis for torrential rainfall patterns identification

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