Stochastic analysis of high flows in some Central British Columbia rivers

High flows derived from the partial duration series were analysed in terms of the probability distributions of magnitude, frequency, duration, and the time of occurrence. Simple methods of representing the timing and duration of high flows as stochastic variables are presented. Existing stochastic models are applied to the probability distributions of the annual frequency of high flows and their magnitudes. A consideration of the statistical properties of the above stochastic variables leads to the development of a technique with which floods exceeding any higher level of interest may be investigated without resorting to a reanalysis of the historical data. The proposed methodology was applied to the daily streamflow records of three rivers located in diverse hydrological environments in Central British Columbia. Good agreement between the computed and the observed data in all eases reflects the applicability of the technique.

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Stochastic analysis of high flows generated by mixed processes

Canadian Journal of Civil Engineering ◽

10.1139/l83-092 ◽

1983 ◽

Vol 10 (4) ◽

pp. 639-648 ◽

Cited By ~ 17

Author(s):

Peter R. Waylen ◽

Ming-ko Woo

Keyword(s):

British Columbia ◽

Stochastic Models ◽

Stochastic Analysis ◽

Historical Data ◽

Probability Distributions ◽

General Applicability ◽

Truncation Level ◽

Partial Duration Series ◽

High Flows ◽

Good Agreement

Existing stochastic models describing the probability distributions of the magnitude, duration, frequency, and timing of high flows resulting from a single flood-generating process are expanded to accommodate the more generalized case of mixed-generating processes. The proposed method is simple to apply and retains a theoretical and physical basis. The ability to predict high flows at any higher truncation level of interest without resorting to reanalysis of the historical data is presented. The methodology is applied to three basins in southwestern British Columbia that experience both snowmelt and rainfall generated floods. Good agreement between the data derived for the partial duration series of the study basins and the predicted distributions illustrates the general applicability of the technique.

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Discussion: Stochastic analysis of high flows in some Central British Columbia rivers

Canadian Journal of Civil Engineering ◽

10.1139/l84-021 ◽

1984 ◽

Vol 11 (1) ◽

pp. 133-134

Author(s):

P. F. Doyle

Keyword(s):

British Columbia ◽

Stochastic Analysis ◽

High Flows

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Nonlinear Ship Loads: Stochastic Models for Extreme Response

Journal of Ship Research ◽

10.5957/jsr.1998.42.1.46 ◽

1998 ◽

Vol 42 (01) ◽

pp. 46-55

Author(s):

Rune Torhaug ◽

Steven R. Winterstein ◽

Arne Braathen

Keyword(s):

Time Domain ◽

Stochastic Models ◽

Stochastic Analysis ◽

Short Wave ◽

Response Analysis ◽

Correct Result ◽

Sea State ◽

Extreme Response ◽

Computational Resources ◽

The Cost

In this study we focus on stochastic analysis methods for selective simulations, and we consider the extreme midspan moment of a fast-moving ship subjected to random Gaussian waves. We concentrate on analysis within a stationary sea state and our purpose is to accurately estimate hourly maximum ship response (compared with the correct result per hour) within a sea state with as little computational resources as possible. We consider how the use of a limited number of short simulations with "critical wave episodes" (short wave segments which are likely candidates to produce extreme response in the simulated hour-long history) reduces the cost of nonlinear time-domain ship response analysis.

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Characterizing wind gusts in complex terrain

Atmospheric Chemistry and Physics ◽

10.5194/acp-19-3797-2019 ◽

2019 ◽

Vol 19 (6) ◽

pp. 3797-3819 ◽

Cited By ~ 8

Author(s):

Frederick Letson ◽

Rebecca J. Barthelmie ◽

Weifei Hu ◽

Sara C. Pryor

Keyword(s):

Wind Turbines ◽

Complex Terrain ◽

Probability Distributions ◽

Wind Loading ◽

Wind Gust ◽

Wind Gusts ◽

Sonic Anemometers ◽

Vertical Displacements ◽

Structural Loading ◽

Good Agreement

Abstract. Wind gusts are a key driver of aerodynamic loading, especially for tall structures such a bridges and wind turbines. However, gust characteristics in complex terrain are not well understood and common approximations used to describe wind gust behavior may not be appropriate at heights relevant to wind turbines and other structures. Data collected in the Perdigão experiment are analyzed herein to provide a foundation for improved wind gust characterization and process-level understanding of flow intermittency in complex terrain. High-resolution observations from sonic anemometers and vertically pointing Doppler lidars are used to conduct a detailed study of gust characteristics with a specific focus on the parent distributions of nine gust parameters (that describe velocity, time, and length scales), their joint distributions, height variation, and coherence in the vertical and horizontal planes. Best-fit distributional forms for varying gust properties show good agreement with those from previous experiments in moderately complex terrain but generate nonconservative estimates of the gust properties that are of key importance to structural loading. Probability distributions of gust magnitude derived from vertically pointing Doppler lidars exhibit good agreement with estimates from sonic anemometers despite differences arising from volumetric averaging and the terrain complexity. Wind speed coherence functions during gusty periods (which are important to structural wind loading) are similar to less complex sites for small vertical displacements (10 to 40 m), but do not exhibit an exponential form for larger horizontal displacements (800 to 1500 m).

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ALTERNATIVE ANALYSIS OF FINITE-TIME PROBABILITY DISTRIBUTIONS OF RENEWAL THEORY

Probability in the Engineering and Informational Sciences ◽

10.1017/s0269964813000417 ◽

2014 ◽

Vol 28 (2) ◽

pp. 183-201 ◽

Cited By ~ 1

Author(s):

Percy H. Brill

Keyword(s):

Operations Research ◽

Stochastic Models ◽

Probability Distributions ◽

Renewal Theory ◽

Regenerative Process ◽

Renewal Processes ◽

Level Crossing ◽

Alternative Analysis ◽

Level Crossing Analysis ◽

Total Life

We introduce a level-crossing analysis of the finite time-t probability distributions of the excess life, age, total life, and related quantities of renewal processes. The technique embeds the renewal process as one cycle of a regenerative process with a barrier at level t, whose limiting probability density function leads directly to the time-t quantities. The new method connects the analysis of renewal processes with the analysis of a large class of stochastic models of Operations Research. Examples are given.

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Identifiability in Stochastic Models: Characterization of Probability Distributions.

Journal of the Royal Statistical Society Series D (The Statistician) ◽

10.2307/2348363 ◽

1994 ◽

Vol 43 (2) ◽

pp. 341 ◽

Cited By ~ 1

Author(s):

T. Subba Rao ◽

B. L. S. Prakasa Rao

Keyword(s):

Stochastic Models ◽

Probability Distributions ◽

Characterization Of Probability Distributions

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Defusing Technology:Technology Diffusion in British Columbia

International Journal of Technology Assessment in Health Care ◽

10.1017/s0266462300003020 ◽

1993 ◽

Vol 9 (1) ◽

pp. 46-61 ◽

Cited By ~ 5

Author(s):

Arminée Kazanjian ◽

Kathryn Friesen

Keyword(s):

British Columbia ◽

Historical Data ◽

Current Data ◽

Fiscal Year ◽

Data Sets ◽

Canadian Province ◽

Data Set ◽

Time Periods ◽

Institutional Profile ◽

Areas Of Interest

AbstractIn order to explore the diffusion of the selected technologies in one Canadian province (British Columbia), two administrative data sets were analyzed. The data included over 40 million payment records for each fiscal year on medical services provided to British Columbia residents (2,968,769 in 1988) and information on physical facilities, services, and personnel from 138 hospitals in the province. Three specific time periods were examined in each data set, starting with 1979–80 and ending with the most current data available at the time. The detailed retrospective analysis of laboratory and imaging technologies provides historical data in three areas of interest: (a) patterns of diffusion and volume of utilization, (b) institutional profile, and (c) provider profile. The framework for the analysis focused, where possible, on the examination of determinants of diffusion that may be amenable to policy influence.

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Integration of Stochastic Models by Minimizing α-Divergence

Neural Computation ◽

10.1162/neco.2007.19.10.2780 ◽

2007 ◽

Vol 19 (10) ◽

pp. 2780-2796 ◽

Cited By ~ 59

Author(s):

Shun-ichi Amari

Keyword(s):

Stochastic Models ◽

Probability Distributions ◽

Predictive Distribution ◽

Natural Generalization ◽

Bayesian Framework ◽

Hellinger Distance ◽

Mixture Of Experts ◽

Mixtures Of Distributions ◽

Leibler Divergence ◽

The Brain

When there are a number of stochastic models in the form of probability distributions, one needs to integrate them. Mixtures of distributions are frequently used, but exponential mixtures also provide a good means of integration. This letter proposes a one-parameter family of integration, called α-integration, which includes all of these well-known integrations. These are generalizations of various averages of numbers such as arithmetic, geometric, and harmonic averages. There are psychophysical experiments that suggest that α-integrations are used in the brain. The α-divergence between two distributions is defined, which is a natural generalization of Kullback-Leibler divergence and Hellinger distance, and it is proved that α-integration is optimal in the sense of minimizing α-divergence. The theory is applied to generalize the mixture of experts and the product of experts to the α-mixture of experts. The α-predictive distribution is also stated in the Bayesian framework.

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