scholarly journals A joint probability approach to flood frequency estimation using Monte Carlo simulation

2010 ◽  
Author(s):  
C. Svensson ◽  
T.R. Kjeldsen ◽  
D.A. Jones
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Frequency Estimation ◽  
Joint Probability ◽  
Flood Frequency ◽  
Probability Approach

Operational Risk Evaluation of an Ice Feature Impact

1990 ◽  
Vol 112 (1) ◽  
pp. 96-101
Author(s):  
A. B. Dunwoody
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probability Distribution ◽  
Stochastic Model ◽  
Risk Evaluation ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Offshore Structure ◽  
Simple Stochastic Model ◽  
General Method

The risk of impact by a particular ice feature in the vicinity of an offshore structure or stationary vessel is of concern during operations. A general method is presented for calculating the risk of an impact in terms of the joint probability distribution of the forecast positions and velocities of the ice feature. A simple stochastic model of the motion of an ice feature is introduced for which the joint probability distribution of ice feature position and velocity can be determined as a function of time. The risk of an impact is presented for this model of the motion of an ice feature. Predictions of the distributions of the time until impact and the drift speed upon impact are also presented and discussed. Predictions are compared against results of a Monte Carlo simulation.

Monte Carlo simulation of flood frequency curves from rainfall

2002 ◽  
Vol 256 (3-4) ◽  
pp. 196-210 ◽  
Author(s):  
A Rahman ◽  
P.E Weinmann ◽  
T.M.T Hoang ◽  
E.M Laurenson
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Flood Frequency ◽  
Frequency Curves

Probability Approach to Casing Design Using Monte Carlo Simulation

2020 ◽  
Author(s):  
Amalachukwu Muoghalu ◽  
John Ansa ◽  
Adewale Dosunmu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probability Approach

Application of the URBS-Monte Carlo Simulation Technique to Urban Catchments: A Case Study for the Coomera River Catchment in Gold Coast Australia

2007 ◽  
Vol 2 (2) ◽  
Author(s):  
Ataur Rahman ◽  
Don Carroll ◽  
Parvez Mahbub ◽  
Sayed Khan ◽  
Khondker Rahman
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Flood Frequency ◽  
Simulation Technique ◽  
Gold Coast ◽  
Design Flood ◽  
River Catchment ◽  
Monte Carlo Simulation Technique ◽  
Urban Catchments ◽  
The Impact

In recent years in Australia, there has been significant research and interest in the development and application of a more holistic approach of design flood estimation such as the Monte Carlo Simulation Technique. The advantage of the Monte Carlo Simulation Technique is that this considers the probabilistic nature of the model input variables in explicit manner as opposed to the Design Event Approach. This paper presents the application of the Monte Carlo Simulation Technique to the Coomera River Catchment in the Gold Coast region Australia. This identifies the probability distributions of rainfall duration, rainfall intensity, rainfall temporal pattern and initial loss from the observed pluviograph and streamflow data in the catchment and applies URBS model to simulate ten thousand streamflow hydrographs to determine derived flood frequency curve for the catchment. It has been found that the URBS-Monte Carlo Simulation Technique (UMCST) provides a robust means for providing a range of inflows to hydraulic/floodplain models to assess the impact of ‘100 year’ storms on the floodplain. Further it is noted that the UMCST technique provides design peak flow rates similar to the Design Event Approach using the temporal patterns derived from the local pluviograph stations.

scholarly journals A Monte Carlo simulation to validate the EAR cut-point method for assessing the prevalence of nutrient inadequacy at the population level

2004 ◽  
Vol 7 (7) ◽  
pp. 893-900 ◽  
Author(s):  
B de Lauzon ◽  
JL Volatier ◽  
A Martin
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Population Level ◽  
Point Method ◽  
Probabilistic Methods ◽  
Recommended Dietary Allowance ◽  
The Real ◽  
Cut Point ◽  
Probability Approach ◽  
Dietary Allowance

AbstractObjective:The aim of this study was to validate the EAR cut-point method for assessing the prevalence of nutrient inadequacy at the population level.Design and subjects:Different methods for estimating the prevalence of inadequate intake were compared: the cut-off point method, with cut-off points at the Recommended Dietary Allowance (RDA), 0.66 RDA, 0.50 RDA and the Estimated Average Requirement (EAR); the probability approach; and a Monte Carlo simulation. In total, 591 men and 674 women, aged 20–55 years, were included in the analyses.Results:The prevalence of inadequate intake as estimated by the EAR cut-point method was similar to the prevalence of inadequacy estimated by both probabilistic methods. The cut-point method with RDA, 0.66 RDA and 0.50 RDA as cut-off limits induced an over- or an underestimation of the real prevalence of inadequacy.Conclusions:Probabilistic methods consider both the intake variability and the requirement variability, and, as a result, their estimation should be closer to the real prevalence of inadequacy. The use of the EAR cut-point method yields a good estimation of the prevalence of inadequate intake, comparable to the probability approach, and limits over- and underestimation of the prevalence induced by other cut-off points.

scholarly journals A Monte Carlo simulation of multivariate general Pareto distribution and its application

2014 ◽  
Vol 11 (6) ◽  
pp. 2733-2753 ◽  
Author(s):  
L. Yao ◽  
W. Dongxiao ◽  
Z. Zhenwei ◽  
H. Weihong ◽  
S. Hui
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Pareto Distribution ◽  
Joint Probability ◽  
Simulation Method ◽  
Value Theory ◽  
Extreme Value ◽  
Marine Environmental ◽  
General Pareto Distribution ◽  
Hydrological Station

Abstract. This paper presents a multivariate general Pareto distribution (MGPD) method and builds a method for solving MGPD through the use of a Monte Carlo simulation for marine environmental extreme-value parameters. The simulation method has proven to be feasible in the analysis of the joint probability of wave height and its concomitant wind from a hydrological station in the South China Sea (SCS). The MGPD is the natural distribution of the multivariate peaks-over-threshold (MPOT) sampling method, and is based on the extreme-value theory. The existing dependence functions can be used in the MGPD, so it may describe more variables which have different dependence relationships. The MGPD method improves the efficiency of the extremes in raw data. For the wave and the concomitant wind from a period of 23 years (1960–1982), the number of the wave and wind selected is averaged to 19 per year. For the joint conditional probability of the MGPD, the relative error is rather small in the Monte Carlo simulation method.

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