Statistical Modeling for One Hour Rainfall Data in Kuala Lumpur and Selangor

2014 ◽  
Vol 1030-1032 ◽  
pp. 665-668
Author(s):  
Amanda Lee Sean Peik ◽  
Choong Wee Kang ◽  
Andy Chan
Keyword(s):  
Extreme Value Distribution ◽  
Extreme Rainfall ◽  
Extreme Value ◽  
Distribution Functions ◽  
Rainfall Data ◽  
Kuala Lumpur ◽  
Maximum Rainfall ◽  
Two Phases ◽  
Annual Maximum Rainfall ◽  
The One

The purpose of this study is to assess patterns of extreme rainfall and this study focused on the changes between two phases for extreme rainfall, for the period of 1971 to 2011 and from 1995 to 2011 in Kuala Lumpur and Selangor. The generalised extreme value distribution appears to outperform other distribution functions such as two-parameter Gumbel and lognormal and the three-parameter generalized extreme value (GEV), lognormal (LN3) and log Pearson (LP3) in modeling the one-hour annual maximum rainfall series from 14 stations. The estimated return period of 20, 50, 100-year for each stations based on the best fitting model for the periods of entire record data and from 1995-2011 have been computed. More than 70% of estimated quantiles using rainfall data from 1995-2011 are higher compared to estimation using the entire recorded data.

scholarly journals Analysis of extreme rainfall in Oti River Basin (West Africa)

2021 ◽  
Author(s):  
Komi S. Klassou ◽  
Kossi Komi
Keyword(s):  
River Basin ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Value Theory ◽  
Extreme Value ◽  
Return Periods ◽  
Maximum Rainfall ◽  
Daily Data ◽  
Dry Spell ◽  
Annual Maximum Rainfall

Abstract Understanding how extreme rainfall is changing locally is a useful step in the implementation of efficient adaptation strategies to negative impacts of climate change. This study aims to analyze extreme rainfall over the middle Oti River Basin. Ten moderate extreme precipitation indices as well as heavy rainfall of higher return periods (25, 50, 75, and 100 years) were calculated using observed daily data from 1921 to 2018. In addition, Mann–Kendall and Sen's slope tests were used for trend analysis. The results showed decreasing trends in most of the heavy rainfall indices while the dry spell index exhibited a rising trend in a large portion of the study area. The occurrence of heavy rainfall of higher return periods has slightly decreased in a large part of the study area. Also, analysis of the annual maximum rainfall revealed that the generalized extreme value is the most appropriate three-parameter frequency distribution for predicting extreme rainfall in the Oti River Basin. The novelty of this study lies in the combination of both descriptive indices and extreme value theory in the analysis of extreme rainfall in a data-scarce river basin. The results are useful for water resources management in this area.

scholarly journals Modelling Extreme Rainfall Using Adjusted Sandwich Estimator

2020 ◽  
pp. 1-5
Author(s):  
Nur Farhanah Kahal Musakkal ◽  
Darmesah Gabda
Keyword(s):  
Maximum Likelihood ◽  
Extreme Rainfall ◽  
Extreme Value ◽  
Annual Maximum ◽  
Generalized Extreme Value ◽  
Maximum Rainfall ◽  
Penalized Maximum Likelihood ◽  
Sandwich Estimator ◽  
Annual Maximum Rainfall ◽  
Site Dependence

The Generalized Extreme Value (GEV) distribution is often used to describe the frequency of occurrence of extreme rainfall. Modelling the extreme event using the independent Generalized Extreme Value to spatial data fails to account the behaviour of dependency data. However, the wrong statistical assumption by this marginal approach can be adjusted using sandwich estimator. In this paper, we used the conventional method of the marginal fitting of generalized extreme value distribution to the extreme rainfall then corrected the standard error to account for inter-site dependence. We also applied the penalized maximum likelihood to improve the generalized parameter estimations. A case study of annual maximum rainfall from several stations at western Sabah is studied, and the results suggest that the variances were found to be greater than the standard error in the marginal estimation as the inter-site dependence being considered. Key words: Generalized Extreme Value theory, sandwich estimator, penalized maximum likelihood, annual maximum rainfall

DIscrepancy of Gear Tooth Allowable Bending Stress Using the Extreme Value Distribution From Different Test Methods

1991 ◽  
Author(s):  
Chienann A. Hou ◽  
Shijun Ma
Keyword(s):  
Gear Tooth ◽  
Extreme Value Distribution ◽  
Test Methods ◽  
Extreme Value ◽  
Distribution Functions ◽  
Value Distribution ◽  
Test Method ◽  
Bending Stress ◽  
Gear Design ◽  
Experimental Approaches

Abstract The allowable bending stress Se of a gear tooth is one of the basic factors in gear design. It can be determined by either the pulsating test or the gear-running test. However, some differences exist between the allowable bending stress Se obtained from these different test methods. In this paper, the probability distribution functions corresponding to each test method are analyzed and the expressions for the minimum extreme value distribution are presented. By using numerical integration, Se values from the population of the same tested gear tooth are obtained. Based on this investigation it is shown that the differences in Se obtained from the different test methods are significant. A proposed correction factor associated with the different experimental approaches is also presented.

scholarly journals Mapping an index of extreme rainfall across the UK

1998 ◽  
Vol 2 (2/3) ◽  
pp. 183-194 ◽  
Author(s):  
D. S Faulkner ◽  
C. Prudhomme
Keyword(s):  
Regression Models ◽  
Digital Terrain Model ◽  
Extreme Rainfall ◽  
Maximum Rainfall ◽  
Mountainous Regions ◽  
Terrain Model ◽  
Rainfall Frequency ◽  
Flood Estimation ◽  
Annual Maximum Rainfall ◽  
The Uk

Abstract. Distance from the sea, proximity of mountains, continentality and elevation are all useful covariates to assist the mapping of extreme rainfalls. Regression models linking these and other variables calculated from a digital terrain model have been built for estimating the median annual maximum rainfall, RMED. This statistic, for rainfall durations between 1 hour and 8 days, is the index variable in the rainfall frequency analysis for the new UK Flood Estimation Handbook. The interpolation of RMED between raingauge sites is most challenging in mountainous regions, which combine the greatest variation in rainfall with the sparsest network of gauges. Sophisticated variables have been developed to account for the influence of topography on extreme rainfall, the geographical orientation of the variables reflecting the prevailing direction of rain-bearing weather systems. The different processes of short and long-duration extreme rainfall are accounted for by separate regression models. The technique of georegression combines estimates from regression models with a map of correction factors interpolated between raingauge locations using the geostatistical method of kriging, to produce final maps of RMED across the UK.

scholarly journals Calibration of NSRP Models from Extreme Value Distributions

Hydrology ◽  
2019 ◽  
Vol 6 (4) ◽  
pp. 89 ◽  
Author(s):  
De Luca ◽  
Galasso
Keyword(s):  
High Resolution ◽  
Probability Distributions ◽  
Mathematical Expression ◽  
Extreme Value ◽  
Extreme Value Distributions ◽  
Data Set ◽  
Maximum Rainfall ◽  
High Resolution Data ◽  
Frequency Distributions ◽  
Annual Maximum Rainfall

In this work, the authors investigated the feasibility of calibrating a model which is suitable for the generation of continuous high-resolution rainfall series, by using only data from annual maximum rainfall (AMR) series, which are usually longer than continuous high-resolution data, or they are the unique available data set for many locations. In detail, the basic version of the Neyman–Scott Rectangular Pulses (NSRP) model was considered, and numerical experiments were carried out, in order to analyze which parameters can mostly influence the extreme value frequency distributions, and whether heavy rainfall reproduction can be improved with respect to the usual calibration with continuous data. The obtained results were highly promising, as the authors found acceptable relationships among extreme value distributions and statistical properties of intensity and duration for the pulses. Moreover, the proposed procedure is flexible, and it is clearly applicable for a generic rainfall generator, in which probability distributions and shape of the pulses, and extreme value distributions can assume any mathematical expression.

Temporal and spatial variations in annual rainfall distribution in Erbil province

2018 ◽  
Vol 47 (1) ◽  
pp. 59-67
Author(s):  
Tariq H Karim ◽  
Dawod R Keya ◽  
Zahir A Amin
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Kendall Test ◽  
Distribution Functions ◽  
Annual Rainfall ◽  
Maximum Rainfall ◽  
Probability Distribution Functions ◽  
Goodness Of Fit Tests ◽  
Annual Maximum Rainfall ◽  
Best Fit

This study aimed to determine the best fit probability distribution of annual maximum rainfall using data from nine stations within Erbil province using different statistical analyses. Nine commonly used probability distribution functions, namely Normal, Lognormal (LN), one-parameter gamma (1P-G), 2P-G, 3P-G, Log Pearson, Weibull, Pareto, and Beta, were assessed. On the basis of maximum overall score, obtained by adding individual point scores from three selected goodness-of-fit tests, the best fit probability distribution was identified. Results showed that the 2P-G distribution and LN distribution were the best fit probability distribution functions for annual rainfall for the region. The analysis of annual rainfall records in Erbil city spanning from 1964 to 2013, covering three periods, also revealed significant temporal changes in the shape and scale parameter patterns of the fitted gamma distribution. Based on the reliable annual rainfall data in the region, the shape and scale parameters were then regionalized, hence it is possible to find the parameter values for any desired location within the study area. The Mann–Kendall test results indicated that there was a decreasing trend in rainfall over most of the study area in recent decades.

scholarly journals Bayesian Modelling of Extreme Rainfall Data of Some Selected Locations in Nigeria

2020 ◽  
pp. 16-26
Author(s):  
Olawale Basheer Akanbi
Keyword(s):  
Climate Change ◽  
Bayesian Methods ◽  
Fossil Fuels ◽  
Extreme Rainfall ◽  
Rainfall Data ◽  
Bayesian Modelling ◽  
Data Set ◽  
Maximum Rainfall ◽  
Block Maxima ◽  
Informative Approach

Climate change occurs when there is rise in average surface temperature on earth, which is mostly due to the burning of fossil fuels usually by human activities. It has been known to contribute greatly to the occurrence of extreme storms and rainfall, this trend continues as the effect of climate change becomes more pronounced. Therefore, this study modelled the extreme rainfall data of three locations (Calabar, Ikeja, Edo) in Nigeria. The block maxima method was used to pick out the maximum rainfall data in each year to form annual maxima data set. The parameters [location, scale, shape] were estimated using both the Classical and Bayesian methods. The result shows that the Bayesian Informative approach is a very good procedure in modelling the Nigerian Extreme Rainfall data.

scholarly journals Statistical modelling of extreme rainfall in Peninsular Malaysia

2021 ◽  
Vol 36 ◽  
pp. 01012
Author(s):  
Wei Lun Tan ◽  
Woon Shean Liew ◽  
Lloyd Ling
Keyword(s):  
Predictive Analytics ◽  
Extreme Value Distribution ◽  
Extreme Rainfall ◽  
Statistical Modelling ◽  
Flash Floods ◽  
Extreme Value ◽  
Peninsular Malaysia ◽  
Generalized Extreme Value Distribution ◽  
Generalized Extreme Value ◽  
Rainfall Model

Flash floods are known as one of the common natural disasters that cost over billions of Ringgit Malaysia throughout history. Academically, an extreme rainfall model is effective in modelling to predict and prevent the occurrence of flash floods. This paper compares four probability distributions, namely, exponential distribution, generalized extreme value distribution, gamma distribution, and Weibull distribution, with the rainfall data of 10 stations in peninsular Malaysia. The period of the data is from 1975 to 2008. The comparison is based on the descriptive and predictive analytics of the models. The determination of the most effective model is through Kolmogorov-Smirnov, Anderson-Darling, and chi-square test. The result shows that generalized extreme value is the most preferred extreme rainfall model for the rainfall cases in Peninsular Malaysia.

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