Extreme Values Theory and Return Level Analysis for Catastrophe Prediction

2013 ◽  
Author(s):  
Amitesh Kapoor ◽  
Utkarsh Shrivastava
Keyword(s):  
Extreme Values ◽  
Return Level ◽  
Extreme Values Theory ◽  
Level Analysis

Extreme Values Theory and Return Level Analysisfor Catastrophe Prediction

2014 ◽  
Vol 23 (2) ◽  
pp. 124-135
Author(s):  
Amitesh Kapoor ◽  
Utkarsh Shrivastava
Keyword(s):  
Extreme Values ◽  
Return Level ◽  
Extreme Values Theory

Risk Measurement Using Extreme Values Theory

2017 ◽  
Author(s):  
Sebastian Maio ◽  
pablo macri ◽  
Manuel Maurette
Keyword(s):  
Extreme Values ◽  
Risk Measurement ◽  
Extreme Values Theory

scholarly journals Return Level Analysis of the Hanumante River Using Structured Expert Judgment: A Reconstruction of Historical Water Levels

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3229
Author(s):  
Paulina E. Kindermann ◽  
Wietske S. Brouwer ◽  
Amber van Hamel ◽  
Mick van Haren ◽  
Rik P. Verboeket ◽  
...  
Keyword(s):  
Water Level ◽  
Historical Data ◽  
Classical Model ◽  
Expert Judgment ◽  
Water Levels ◽  
Return Level ◽  
Considerable Uncertainty ◽  
Level Data ◽  
Rain Events ◽  
Level Analysis

Like other cities in the Kathmandu Valley, Bhaktapur faces rapid urbanisation and population growth. Rivers are negatively impacted by uncontrolled settlements in flood-prone areas, lowering permeability, decreasing channels widths, and waste blockage. All these issues, along with more extreme rain events during the monsoon due to climate change, have led to increased flooding in Bhaktapur, especially by the Hanumante River. For a better understanding of flood risk, the first step is a return level analysis. For this, historical data are essential. Unfortunately, historical records of water levels are non-existent for the Hanumante River. We measured water levels and discharge on a regular basis starting from the 2019 monsoon (i.e., June). To reconstruct the missing historical data needed for a return level analysis, this research introduces the Classical Model for Structured Expert Judgment (SEJ). By employing SEJ, we were able to reconstruct historical water level data. Expert assessments were validated using the limited data available. Based on the reconstructed data, it was possible to estimate the return periods of extreme water levels of the Hanumante River by fitting a Generalized Extreme Value (GEV) distribution. Using this distribution, we estimated that a water level of about 3.5 m has a return period of ten years. This research showed that, despite considerable uncertainty in the results, the SEJ method has potential for return level analyses.

scholarly journals Maximum Values of Frozen Soil’s Depth and Hoar Frost’s Thickness Estimated on the Basis of Extreme Values Theory

2018 ◽  
Vol 12 (2) ◽  
pp. 13-23
Author(s):  
Maria Nedealcov ◽  
Valentin Răileanu ◽  
Gheorghe Croitoru ◽  
Cojocari Rodica ◽  
Crivova Olga
Keyword(s):  
Risk Factors ◽  
Extreme Value Theory ◽  
Extreme Values ◽  
Gumbel Distribution ◽  
Value Theory ◽  
Extreme Value ◽  
Frozen Soil ◽  
Return Interval ◽  
The Mean ◽  
Extreme Values Theory

Abstract Extreme climatic phenomena present risk factors for agriculture, health, constructions, etc. and are studied profoundly these past years using extreme value theory. Several relation that describe positive extreme values’ probability Generalized Extreme Value and Gumbel distribution are presented in the article. As a example, we show the maps of characteristic and reference values of the maximum depth of the frozen soil and thickness of hoar-frost with a probability of exceeding per year equal to 0,02, which is equivalent to the mean return interval of 50 years. The obtained results could serve as a base for elaboration of national annexes in constructions.

scholarly journals Data Geometry and Extreme Value Distribution

2021 ◽  
Vol 2 (2) ◽  
pp. 06-15
Author(s):  
Mamadou Cisse ◽  
Aliou Diop ◽  
Souleymane Bognini ◽  
Nonvikan Karl-Augustt ALAHASSA
Keyword(s):  
Weibull Distribution ◽  
Extreme Values ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Data System ◽  
Peaks Over Threshold ◽  
Stochastic Graphs ◽  
Block Maxima ◽  
Extreme Values Theory

In extreme values theory, there exist two approaches about data treatment: block maxima and peaks-over-threshold (POT) methods, which take in account data over a fixed value. But, those approaches are limited. We show that if a certain geometry is modeled with stochastic graphs, probabilities computed with Generalized Extreme Value (GEV) Distribution can be deflated. In other words, taking data geometry in account change extremes distribution. Otherwise, it appears that if the density characterizing the states space of data system is uniform, and if the quantile studied is positive, then the Weibull distribution is insensitive to data geometry, when it is an area attraction, and the Fréchet distribution becomes the less inflationary.

scholarly journals Canadian RCM Projected Transient Changes to Precipitation Occurrence, Intensity, and Return Level over North America

2015 ◽  
Vol 28 (17) ◽  
pp. 6920-6937 ◽  
Author(s):  
Jonathan Jalbert ◽  
Anne-Catherine Favre ◽  
Claude Bélisle ◽  
Jean-François Angers ◽  
Dominique Paquin
Keyword(s):  
North America ◽  
Climate Model ◽  
Extreme Values ◽  
Regional Climate ◽  
Return Level ◽  
Nonstationary Processes ◽  
Accurate Analysis ◽  
Precipitation Occurrence ◽  
Time Period ◽  
Precipitation Changes

Abstract As a consequence of the increase in atmospheric greenhouse gas concentrations, potential changes in both precipitation occurrence and intensity may lead to several consequences for Earth’s environment. It is therefore relevant to estimate these changes in order to anticipate their consequences. Many studies have been published on precipitation changes based on climate simulations. These studies are almost always based on time slices; precipitation changes are estimated by comparing two 30-yr windows. To this extent, it is commonly assumed that nonstationary processes are not significant for such a 30-yr slice. Thus, it frees the investigator to statistically model nonstationary processes. However, using transient runs instead of time slices surely leads to more accurate analysis since more data are taken into account. Therefore, the aim of the present study was to develop a transient probabilistic model for describing simulated daily precipitation from the Canadian Regional Climate Model (CRCM) in order to investigate precipitation evolution over North America. Changes to both the occurrence and intensity of precipitation are then assessed from a continuous time period. Extreme values are also investigated with the transient run; a new methodology using the models for precipitation occurrence and intensity was developed for achieving nonstationary frequency analysis. The results herein show an increase in both precipitation occurrence and intensity for most parts of Canada while a decrease is expected over Mexico. For the continental United States, a decrease in both occurrence and intensity is expected in summer but an increase is expected in winter.

scholarly journals Computing absorbing times via fluid approximations

2017 ◽  
Vol 49 (3) ◽  
pp. 768-790 ◽  
Author(s):  
Nicolas Gast ◽  
Bruno Gaujal
Keyword(s):  
Extreme Values ◽  
Mutual Exclusion ◽  
High Dimensional ◽  
Fluid Approximation ◽  
Discrete Time Markov Chain ◽  
Absorbing State ◽  
Fluid Approximations ◽  
Coupon Collector ◽  
Extreme Values Theory ◽  
First Time

AbstractIn this paper we compute the absorbing timeTnof ann-dimensional discrete-time Markov chain comprisingncomponents, each with an absorbing state and evolving in mutual exclusion. We show that the random absorbing timeTnis well approximated by a deterministic timetnthat is the first time when a fluid approximation of the chain approaches the absorbing state at a distance 1 /n. We provide an asymptotic expansion oftnthat uses the spectral decomposition of the kernel of the chain as well as the asymptotic distribution ofTn, relying on extreme values theory. We show the applicability of this approach with three different problems: the coupon collector, the erasure channel lifetime, and the coupling times of random walks in high-dimensional spaces.

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