scholarly journals Survey of stochastic models for wind and sea state time series

2007 ◽  
Vol 22 (2) ◽  
pp. 113-126 ◽  
Author(s):  
V. Monbet ◽  
P. Ailliot ◽  
M. Prevosto
Keyword(s):  
Time Series ◽  
Stochastic Models ◽  
Sea State ◽  
State Time

Nonlinear Simulation of Multivariate Sea State Time Series

2005 ◽  
Author(s):  
Vale´rie Monbet ◽  
Pierre Ailliot ◽  
Marc Prevosto
Keyword(s):  
Time Series ◽  
Markov Chains ◽  
Gaussian Process ◽  
Autoregressive Model ◽  
Transition Probabilities ◽  
Sea State ◽  
Nonlinear Simulation ◽  
Nonlinear Methods ◽  
State Time ◽  
Local Grid

In this paper, three nonlinear methods are described for artificially generating operational sea state histories. In the first method, referred to as Translated Gaussian Process the observed time series is transformed to a process which is supposed to be Gaussian. This Gaussian process is simulated and back transformed. The second method, called Local Grid Bootstrap, consists in a resampling algorithm for Markov chains within which the transition probabilities are estimated locally. The last models is a Markov Switching Autoregressive model which allows in particular to model different weather types.

Nonlinear Ship Loads: Stochastic Models for Extreme Response

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.

Intercomparison of Seastate and Zerocrossing Parameters From the WACSIS Field Experiment and Interpretation Using Video Evidence

2002 ◽  
Author(s):  
Stephen F. Barstow ◽  
Harald E. Krogstad ◽  
Lasse Lo̸nseth ◽  
Jan Petter Mathisen ◽  
Gunnar Mo̸rk ◽  
...  
Keyword(s):  
Time Series ◽  
The Netherlands ◽  
Field Experiment ◽  
Time Series Data ◽  
Series Data ◽  
Sea State ◽  
Single Wave ◽  
Video Recordings ◽  
Wave Parameters ◽  
Wave Elevation

During the WACSIS field experiment, wave elevation time series data were collected over the period December 1997 to May 1998 on and near the Meetpost Nordwijk platform off the coast of the Netherlands from an EMI laser, a Saab radar, a Baylor Wave Staff, a Vlissingen step gauge, a Marex radar and a Directional Waverider. This paper reports and interprets, with the help of simultaneous dual video recordings of the ocean surface, an intercomparison of both single wave and sea state wave parameters.

Research on Ship Swaying Motion Prediction Based on Multi-Variable Chaotic Time Series Analysis

2013 ◽  
Vol 712-715 ◽  
pp. 1550-1554
Author(s):  
Xin Dong Yang ◽  
Zuo Chao Wang ◽  
Ai Guo Shi ◽  
Bo Liu ◽  
Li Li
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Chaotic Time Series ◽  
Ship Motion ◽  
Ship Motions ◽  
Sea State ◽  
Time Prediction ◽  
Series Analysis ◽  
Short Time ◽  
Chaotic Time Series Analysis

Wind and waves have particularly significant influence upon exertion of naval vessels battle effectiveness. It is urgently necessary to improve the ability of the Navy to carry out combat service in severe sea state normally. This paper aims to obtain the accurate prediction of ship motions with second level predictable time in real waves. According to the characteristics of the ship motion, the research on extremely short-time prediction of ship motion has been carried out based on multi-variable chaotic time series analysis, and the effectiveness of the prediction of ship motion in real wave is highly improved.

scholarly journals CONTRIBUTION OF SEASONAL STOCHASTIC MODELS SARIMA TO THE RATIONAL WATER RESOURCES MANAGEMENT. THE CASE OF THE KRANIA ELASSONA KARST SYSTEM, THESSALY, GREECE

2004 ◽  
Vol 36 (4) ◽  
pp. 2012
Author(s):  
A. Μανάκος ◽  
Γ. Δημόπουλος
Keyword(s):  
Time Series ◽  
Stochastic Model ◽  
Groundwater Flow ◽  
Stochastic Models ◽  
Groundwater Resources ◽  
Spring Discharge ◽  
Karst System ◽  
Flow Conditions ◽  
Discharge Time ◽  
Monthly Discharge

Several stochastic models, known as Box and Jenkins or SARIMA (Seasonal Autoregressive Integrated Moving Average) have been used in the past for forecasting hydrological time series in general and stream flow or spring discharge time series in particular. SARIMA models became very popular because of their simple mathematical structure, convenient representation of data in terms of a relatively small number of parameters and their applicability to stationary as well as nonstationary process.Application of the seasonal stochastic model SARIMA to the spring's monthly discharge time series for the period 1974-1993 in Krania Elassona karst system yielded the following results. Logarithms of the monthly spring discharge time series can be simulated on a SARIMA (4,1,1)(1,1,1)12 type model. This type of model is suitable for the Krania Elassona karst system simulation and can be utilised as a tool to predict monthly discharge values at Kafalovriso spring for at least a 2 year period. Seasonal stochastic models SARIMA seem to be capable of simulating both runoff and groundwater flow conditions on a karst system and also easily adapt to their natural conditions.Adapting the proper stochastic model to the karst groundwater flow conditions offers the possibility to obtain accurate short term predictions, thus contributing to rational groundwater resources exploitation and management planning

On some problems about testing stochastic models of the earthquake time series

1993 ◽  
Vol 7 (2) ◽  
pp. 99-108 ◽  
Author(s):  
Gerassimos A. Papadopoulos
Keyword(s):  
Time Series ◽  
Stochastic Models
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