scholarly journals Multivariate extreme value analysis of storm surges in SCS on peak over threshold method

2015 ◽  
Vol 12 (6) ◽  
pp. 2783-2805
Author(s):  
Y. Luo ◽  
D. Sui ◽  
H. Shi ◽  
Z. Zhou ◽  
D. Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Storm Surge ◽  
Extreme Values ◽  
Storm Surges ◽  
Extreme Value Analysis ◽  
Joint Probability Distribution ◽  
Beibu Gulf ◽  
Threshold Method ◽  
Value Analysis

Abstract. We use a novel statistical approach-MGPD to analyze the joint probability distribution of storm surge events at two sites and present a warning method for storm surges at two adjacent positions in Beibu Gulf, using the sufficiently long field data on surge levels at two sites. The methodology also develops the procedure of application of MGPD, which includes joint threshold and Monte Carlo simulation, to handle multivariate extreme values analysis. By comparing the simulation result with analytic solution, it is shown that the relative error of the Monte Carlo simulation is less than 8.6 %. By running MGPD model based on long data at Beihai and Dongfang, the simulated potential surge results can be employed in storm surge warnings of Beihai and joint extreme water level predictions of two sites.

scholarly journals Numerical Investigation of Storm Surge in Kong Port in the Persian Gulf

2019 ◽  
Author(s):  
Amir Hossein Mahdavi ◽  
Hamid Ansari Sharghi
Keyword(s):  
Sea Level Rise ◽  
Engineering Design ◽  
Sea Level ◽  
Storm Surge ◽  
Storm Surges ◽  
Wave Model ◽  
Water Level Fluctuations ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Mean Sea Level

Storm surge is generated by the integration of waves, tide and wind setup that is resulted in unwanted mean sea level rise and coastal flooding. The estimation of accurate storm surge is essential for the engineering design of coastal structures. In this study, we estimated the respond of mean sea level winds, tide, waves, and sea-level rise using a local coastal model. A fully coupled hydrodynamic and wave model was implemented to obtain storm surge from different phenomena. The simulations of water level fluctuations due to these parameters were analyzed with the wind forces identified with tidal observations in the Port of Kong. Extreme value analysis was performed to determine the fluctuations associated with different return periods. These data were combined by sea-level rise projections are combined with resulted value. The worst and best scenario of storm surges for each return period were determined for engineering design purposes.

scholarly journals UNCERTAINTY AND BIAS IN EXTREME VALUE ANALYSIS OF RECORDS OF STORM SURGES FOR COASTAL PROTECTION PLANS

2018 ◽  
pp. 47
Author(s):  
Toshikazu Kitano
Keyword(s):  
Confidence Interval ◽  
Storm Surge ◽  
Return Period ◽  
Prediction Interval ◽  
Storm Surges ◽  
Extreme Value ◽  
Return Level ◽  
Extreme Value Analysis ◽  
Coastal Protection ◽  
Value Analysis

There are several arguments to be discussed for the probability of hazards due to the storm surge. One is a common point of describing the uncertainty of extreme events, and another point is for the special case due to the storm surge. 1) Return level is one of the important results by extreme value analysis, and the confidence interval also serves us an useful and desirable information for uncertainty. Is it true? The answer is negative. Return period is right, and important. But the confidence interval, in this case, is shown for return level which is the constant value that is significant after the repeating encounters of the exceedance levels over very long period. But it is of our interest to know which value is the successively occurring level in the future return period, which is a stochastic variable. It is not a constant value but unknown even for the God. The prediction interval should be employed for the next realized value of our interest.

Investigating the occurrence, likelihood and regional variability of extreme ozone pollution episodes across the UK

2021 ◽  
Author(s):  
Lily Gouldsbrough ◽  
Ryan Hossaini ◽  
Emma Eastoe ◽  
Paul J. Young
Keyword(s):  
Surface Ozone ◽  
Extreme Value Analysis ◽  
Daily Maximum ◽  
Monitoring Site ◽  
Ozone Pollution ◽  
Threshold Method ◽  
Regional Variability ◽  
Value Analysis ◽  
The Uk ◽  
Pollution Episodes

<p>Warm summer temperatures provide ideal conditions for the occurrence of extreme ground level ozone pollution episodes. Given the well-established negative impacts of ozone on human and plant health, understanding and attributing these extreme events is of importance to the scientific and wider community, particularly as heatwaves may become more frequent due to climate change. Extreme Value Analysis provides a powerful and flexible framework in which to statistically model unusually large observed values of ozone extracted from historical data. Here, a temperature dependent Peaks-Over-Threshold method based upon the Generalised Pareto Distribution is used to carry out a regional comparison of extreme ozone pollution episodes within the UK. Our analysis uses surface ozone observations from the UK’s extensive Automatic Urban and Rural Network. The statistical model was used to quantify the frequency and magnitude of extreme ozone events, including a probabilistic assessment of exceeding UK public health thresholds, conditional on temperature. Return levels are provided for each monitoring site demonstrating the expected future projections of extreme ozone pollution events across the UK. We find that across UK rural background sites, return periods for a daily maximum 8-hr ozone level of 100 ug/m3 (a 'moderate' level of air pollution in the UK's Air Quality Index) range from 32-147 days, based on analysis of the data in the decade 2010-2019. Similarly, for urban background sites the range is 36-869 days. An analysis of the spatio temporal variability in UK ozone extremes, along with their temperature dependence, will be presented.</p>

Seasonal shift in storm surges at Brest revealed by extreme value analysis

2021 ◽  
Author(s):  
Markus Reinert ◽  
Lucia Pineau‐Guillou ◽  
Nicolas Raillard ◽  
Bertrand Chapron
Keyword(s):  
Storm Surges ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Seasonal Shift

Covariate Extreme Value Analysis Using Wave Spectral Partitioning

2020 ◽  
Vol 37 (5) ◽  
pp. 873-888 ◽  
Author(s):  
Jesús Portilla-Yandún ◽  
Edwin Jácome
Keyword(s):  
Extreme Values ◽  
Wind Waves ◽  
Spectral Energy Distribution ◽  
Wave Spectrum ◽  
Wave Climate ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Spectral Energy ◽  
Value Analysis ◽  
Spectral Partitioning

AbstractAn important requirement in extreme value analysis (EVA) is for the working variable to be identically distributed. However, this is typically not the case in wind waves, because energy components with different origins belong to separate data populations, with different statistical properties. Although this information is available in the wave spectrum, the working variable in EVA is typically the total significant wave height Hs, a parameter that does not contain information of the spectral energy distribution, and therefore does not fulfill this requirement. To gain insight in this aspect, we develop here a covariate EVA application based on spectral partitioning. We observe that in general the total Hs is inappropriate for EVA, leading to potential over- or underestimation of the projected extremes. This is illustrated with three representative cases under significantly different wave climate conditions. It is shown that the covariate analysis provides a meaningful understanding of the individual behavior of the wave components, in regard to the consequences for projecting extreme values.

scholarly journals EXTREME VALUE ANALYSIS WITHOUT THE LARGEST VALUES: WHAT CAN BE DONE?

2019 ◽  
Vol 34 (2) ◽  
pp. 200-220
Author(s):  
Jingjing Zou ◽  
Richard A. Davis ◽  
Gennady Samorodnitsky
Keyword(s):  
Order Statistics ◽  
Extreme Values ◽  
Sample Path ◽  
Estimation Procedure ◽  
Extreme Value ◽  
Hill Estimator ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Heavy Tailed ◽  
The Hill

AbstractIn this paper, we are concerned with the analysis of heavy-tailed data when a portion of the extreme values is unavailable. This research was motivated by an analysis of the degree distributions in a large social network. The degree distributions of such networks tend to have power law behavior in the tails. We focus on the Hill estimator, which plays a starring role in heavy-tailed modeling. The Hill estimator for these data exhibited a smooth and increasing “sample path” as a function of the number of upper order statistics used in constructing the estimator. This behavior became more apparent as we artificially removed more of the upper order statistics. Building on this observation we introduce a new version of the Hill estimator. It is a function of the number of the upper order statistics used in the estimation, but also depends on the number of unavailable extreme values. We establish functional convergence of the normalized Hill estimator to a Gaussian process. An estimation procedure is developed based on the limit theory to estimate the number of missing extremes and extreme value parameters including the tail index and the bias of Hill's estimator. We illustrate how this approach works in both simulations and real data examples.

Modeling of Motor Vehicle Claims Using Extreme Value Methodology and Monte Carlo Stimulation: A Comparative Study

2008 ◽  
Vol 4 (4) ◽  
pp. 96-103
Author(s):  
S. Chandrasekhar
Keyword(s):  
Monte Carlo ◽  
Pareto Distribution ◽  
Motor Vehicle ◽  
Claim Data ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Claim Amount ◽  
Insurance Claims ◽  
Value Analysis ◽  
Generalised Pareto Distribution

Motor Vehicle Insurance claims form a substantial component of Non life insurance claims and it is also growing with increasing number of vehicles on roads. It is also desirable to have an idea of what will be the likely claim amount for the coming future (Monthly, Quarterly, Yearly) based on past claim data. If one looks at the claim amount one can make out that there will be few large claims compared to large number of average and below average claims. Thus the distributions of claims do not follow a Symmetric pattern which makes it difficult using normal Statistical analysis. The methodology followed to analyze such data is known as Extreme value Analysis. Extreme value analysis is a general name which covers (i) Generalised Extreme Value (GEV) (ii) Generalised Pareto Distribution (GPD). Basically these techniques can deal with non symmetric shape of the distribution which is close to reality. Normally one fits a generalised Extreme Value distribution (GEV)/Generalised Pareto Distribution (GPD) and using parameters of fitted distribution future, forecast of likely losses can be predicted. Second method of analyzing such data is using methodology of simulation. Here we fit a Poisson distribution for arrival of claims and weibull/pareto/Lognormal for claim amount. Using Monte Carlo Simulation one combines both the distributions for future prediction of claim amount. This paper shows a comparison of the above techniques on motor vehicle claims data.

scholarly journals Extreme Value Analysis for Time-variable Mixed Workload

2021 ◽  
Author(s):  
Szilárd Bozóki ◽  
András Pataricza
Keyword(s):  
Extreme Values ◽  
Real Life ◽  
Extreme Value ◽  
Time Variable ◽  
Extreme Value Analysis ◽  
Computing Systems ◽  
Value Analysis ◽  
Root Cause ◽  
Extreme Value Theorem ◽  
Dynamic Time

Proper timeliness is vital for a lot of real-world computing systems. Understanding the phenomena of extreme workloads is essential because unhandled, extreme workloads could cause violation of timeliness requirements, service degradation, and even downtime. Extremity can have multiple roots: (1) service requests can naturally produce extreme workloads; (2) bursts could randomly occur on a probabilistic basis in case of a mixed workload in multiservice systems; (3) workload spikes typically happen in deadline bound tasks.Extreme Value Analysis (EVA) is a statistical method for modeling the extremely deviant values corresponding to the largest values. The foundation mathematics of EVA, the Extreme Value Theorem, requires the dataset to be independent and identically distributed. However, this is not generally true in practice because, usually, real-life processes are a mixture of sources with identifiable patterns. For example, seasonality and periodic fluctuations are regularly occurring patterns. Deadlines can be purely periodic, e.g., monthly tax submissions, or time variable, e.g., university homework submission with variable semester time schedules.We propose to preprocess the data using time series decomposition to separate the stochastic process causing extreme values. Moreover, we focus on the case where the root cause of the extreme values is the same mechanism: a deadline. We exploit known deadlines using dynamic time warp to search for the recurring similar workload peak patterns varying in time and amplitude.

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