Neural networks for wave forecasting

Deo, M. C.; Jha, Anand K.; Chaphekar, A.S.; Ravikant, K.

doi:10.1016/s0029-8018(00)00027-5

Cited by 259 publications

(88 citation statements)

References 9 publications

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“…Other widely used wave models are Wave Watch and SWAN, and there exist a number of other models as well (The Wise Group et al 2007). However, wave generation is basically an uncertain and random process which makes it difficult to model deterministically, and in Deo et al (2001), Bazargan et al (2007) approaches using neural networks were proposed as an alternative to deterministic wave forecasting models.…”

Section: Short-term Stochastic Wave Modelsmentioning

confidence: 99%

“…For short-term modelling of wave parameters, different approaches of artificial neural networks (see e.g. Deo et al 2001;Mandal and Prabaharan 2006;Arena and Puca 2004;Makarynskyy et al 2005) and data mining techniques (Mahjoobi and Etemad-Shahidi 2008;Mahjoobi and Mosabbeb 2009) have successfully been applied. A non-linear threshold autoregressive model for the significant waveheight was proposed in Scotto and Guedes Soares (2000).…”

Section: Stationary Modelsmentioning

confidence: 99%

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Long-term time-dependent stochastic modelling of extreme waves

Vanem

2010

Stoch Environ Res Risk Assess

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This paper presents a literature survey on timedependent statistical modelling of extreme waves and sea states. The focus is twofold: on statistical modelling of extreme waves and space-and time-dependent statistical modelling. The first part will consist of a literature review of statistical modelling of extreme waves and wave parameters, most notably on the modelling of extreme significant wave height. The second part will focus on statistical modelling of time-and space-dependent variables in a more general sense, and will focus on the methodology and models used also in other relevant application areas. It was found that limited effort has been put on developing statistical models for waves incorporating spatial and long-term temporal variability and it is suggested that model improvements could be achieved by adopting approaches from other application areas. In particular, Bayesian hierarchical space-time models were identified as promising tools for spatio-temporal modelling of extreme waves. Finally, a review of projections of future extreme wave climate is presented.

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Section: Short-term Stochastic Wave Modelsmentioning

confidence: 99%

Section: Stationary Modelsmentioning

confidence: 99%

Long-term time-dependent stochastic modelling of extreme waves

Vanem

2010

Stoch Environ Res Risk Assess

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“…However, computational cost increases exponentially with increasing order of complexity, such as increasing the number of hidden layers [52]. Previous studies using ANN algorithm to establish forecast models suggest that a three-layer structure is capable of generating satisfactory correlations among input and output datasets [22,55,56]. Deo [1] summarized four steps to mathematically obtain outputs using ANN algorithms.…”

Section: Hl(j)mentioning

confidence: 99%

Short-Term Forecasting of Coastal Surface Currents Using High Frequency Radar Data and Artificial Neural Networks

Ren

Hartnett

2018

Remote Sensing

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Accurate and timely information of surface currents is crucial for various operations such as search and rescue, marine renewable energy extraction and oil spill treatment. Conventional approaches to study coastal surface currents are numerical models and observation platforms such as radars and satellites. However, both have limits. To efficiently obtain high accuracy short-term forecasting states of oceanic parameters of interest, a robust soft computing approach-Artificial Neural Networks (ANN)-was applied to predict surface currents in a tide-and wind-dominated coastal area. Hourly observed surface currents from a Coastal Ocean Dynamic Application Radar (CODAR) system, and tide and wind data from forecasting models were used to establish ANN models for Galway Bay area. One of the fastest algorithms, resilient back propagation, was used to adapt all weights and biases. This study focused on investigating the sensitivity of an ANN model to a series of different input datasets. Results indicate that correlation between ANN forecasts and observation was greater than 0.9 for both surface velocity components with one-hour lead time. Strong correlation (≥0.75) was obtained between predicted results and radar data for both surface velocity components with three-hour lead time at best. However, forecasting accuracy deteriorated rapidly with longer lead time. By comparison with previous data assimilation models, in this research, best performance was achieved from ANN model's peak times of the tidally dominant surface velocity component. The forecasts presented in this research show clear improvements over previous attempts at short-term forecasting of wind-and tide-dominated currents using ANN.

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“…In addition, a substantial sample size is strictly required in training ANN models, which may imply that a high computational cost is incurred. For example, Deo et al (2001), Agrawal and Deo (2002), Mandal and Prabaharan (2010), and Kamranzad et al (2011) used 80% of the data to train ANN models.…”

Section: Introductionmentioning

confidence: 99%

A hybrid EMD-AR model for nonlinear and non-stationary wave forecasting

Duan

Huang

2016

JZUS-A

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Abstract:Accurate wave forecasting with a couple of hours of warning time offers improvements in safety for maritime operation-related activities. Autoregressive (AR) model is an efficient and highly adaptive approach for wave forecasting. However, it is based on linear and stationary theory and hence has limitations in forecasting nonlinear and non-stationary waves. Inspired by the capability of empirical mode decomposition (EMD) technique in handling nonlinear and non-stationary signals, this paper describes the development of a hybrid EMD-AR model for nonlinear and non-stationary wave forecasting. The EMD-AR model was developed by coupling an AR model with the EMD technique. Nonlinearity and non-stationarity were overcome by decomposing the wave time series into several simple components for which the AR model is suitable. The EMD-AR model was implemented using measured significant wave height data from the National Data Buoy Center, USA. Prediction results from various locations consistently show that the hybrid EMD-AR model is superior to the AR model. This demonstrates that the EMD technique is effective in processing nonlinear and non-stationary waves.

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Neural networks for wave forecasting

Cited by 259 publications

References 9 publications

Long-term time-dependent stochastic modelling of extreme waves

Long-term time-dependent stochastic modelling of extreme waves

Short-Term Forecasting of Coastal Surface Currents Using High Frequency Radar Data and Artificial Neural Networks

A hybrid EMD-AR model for nonlinear and non-stationary wave forecasting

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