Bivariate autoregressive models for the time series of significant wave height and mean period

Soares, C. Guedes; Cunha, Cleyzer Adrian da

doi:10.1016/s0378-3839(00)00015-6

Cited by 60 publications

(6 citation statements)

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“…AR techniques can be used to determine both wind speed and wave height. Generally, an AR of order 2 is sufficient for wind and an AR model of order 19-20 is required for Hs [41][42][43]. There is also data transformations required for AR use, such as removal of the monthly mean and diurnal variations.…”

Section: Weather and Sea State Modellingmentioning

confidence: 99%

A review of operations and maintenance modelling with considerations for novel wind turbine concepts

McMorland

Flannigan

Carroll

et al. 2022

Renewable and Sustainable Energy Reviews

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Section: Weather and Sea State Modellingmentioning

confidence: 99%

A review of operations and maintenance modelling with considerations for novel wind turbine concepts

McMorland

Flannigan

Carroll

et al. 2022

Renewable and Sustainable Energy Reviews

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“…The autoregressive moving average (ARMA) model, the autoregressive (AR) model, and the autoregressive integrated moving average model (ARIMA) models are classical time series models. The effective wave height of the Portuguese coastline region is the prediction work using AR models (Soares et al, 1996;Soares and Cunha, 2000). used ARIMA and ARMA models to predict waves at multiple intervals along the Indian coast.…”

Section: Figurementioning

confidence: 99%

A review of artificial intelligence in marine science

Song

Pang²,

Hou³

et al. 2023

Front. Earth Sci.

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Utilization and exploitation of marine resources by humans have contributed to the growth of marine research. As technology progresses, artificial intelligence (AI) approaches are progressively being applied to maritime research, complementing traditional marine forecasting models and observation techniques to some degree. This article takes the artificial intelligence algorithmic model as its starting point, references several application trials, and methodically elaborates on the emerging research trend of mixing machine learning and physical modeling concepts. This article discusses the evolution of methodologies for the building of ocean observations, the application of artificial intelligence to remote sensing satellites, smart sensors, and intelligent underwater robots, and the construction of ocean big data. We also cover the method of identifying internal waves (IW), heatwaves, El Niño-Southern Oscillation (ENSO), and sea ice using artificial intelligence algorithms. In addition, we analyze the applications of artificial intelligence models in the prediction of ocean components, including physics-driven numerical models, model-driven statistical models, traditional machine learning models, data-driven deep learning models, and physical models combined with artificial intelligence models. This review shows the growth routes of the application of artificial intelligence in ocean observation, ocean phenomena identification, and ocean elements forecasting, with examples and forecasts of their future development trends from several angles and points of view, by categorizing the various uses of artificial intelligence in the ocean sector.

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“…The most frequently used parametric methods are based on autoregressive models. Studies employing such methods include Guedes Soares and Ferreira [1996], Guedes Soares et al [1996], Scotto and Guedes Soares [2000], Stefanakos [1999], Stefanakos and Athanassoulis [2001], and Cai et al [2007] for univariate series; for multivariate series, relevant studies include Guedes Soares and Cunha [2000], Stefanakos and Athanassoulis [2003], Stefanakos and Belibassakis [2005], and Cai et al [2008]. As in the TGP, before autoregressive models can be used, the series must be normalized.…”

Section: Introductionmentioning

confidence: 99%

Non-stationary wave height climate modeling and simulation

Solari

Losada

2011

J. Geophys. Res.

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[1] The most popular methods of simulating time series for wave heights and other meteorological and oceanic variables are based on the use of autoregressive models and the transformation of variables to make them normal and stationary. Generally, when these models are used, attention is centered on their capacity to represent the autocorrelation of the series. In this article, a simulation model is proposed that is based on the following: (i) a non-stationary parametric mixture model for the marginal distribution of the variable, that combines a log-normal distribution for main-mass regime and generalized Pareto distributions for upper and lower tail regimes, and (ii) the use of copulas to model the time dependency of the variable. The model has been evaluated by comparing the original series and the simulated series in terms of the autocorrelation function, the mean, the annual maxima and peaks-over-threshold regimes, and the persistences regime. It has also been compared to an ARMA model and found to yield more satisfactory results.Citation: Solari, S., and M. A. Losada (2011), Non-stationary wave height climate modeling and simulation, J. Geophys. Res., 116, C09032,

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Bivariate autoregressive models for the time series of significant wave height and mean period

Cited by 60 publications

References 4 publications

A review of operations and maintenance modelling with considerations for novel wind turbine concepts

A review of operations and maintenance modelling with considerations for novel wind turbine concepts

A review of artificial intelligence in marine science

Non-stationary wave height climate modeling and simulation

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