Analysis of feed forward and recurrent neural networks in predicting the significant wave height at the moored buoys in Bay of Bengal

A large number of ocean activities call for real time or on-line forecasting of wind wave characteristics including significant wave height (Hs). The work reported in this paper uses statistics, and artificial neural networks trained with an optimization technique called simulated annealing to estimate the parameters of a probability distribution called hepta-parameter spline for the conditional probability density functions (pdf’s) of significant wave heights given their eight immediate preceding 3-hourly observed Hs’s. These pdf’s are used in the simulation of significant wave heights related to a location in the Pacific. The paper also deals with short and long term forecasting of Hs for the region through generating random variates from the spline distribution.

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Prediction of the wave power in the Black Sea based on wind speed using artificial neural networks

E3S Web of Conferences ◽

10.1051/e3sconf/20185101006 ◽

2018 ◽

Vol 51 ◽

pp. 01006

Author(s):

Sorin Ciortan ◽

Eugen Rusu

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Wind Speed ◽

Wave Height ◽

Black Sea ◽

Significant Wave Height ◽

The Black Sea ◽

Wave Power ◽

Significant Wave ◽

Artificial Neural

The paper proposes a prediction methodology for the significant wave height (and implicitly the wave power), based on the artificial neural networks. The proposed approach takes as input data the wind speed values recorded for different time periods. The prediction of significant wave height is useful both for assessment of wave energy as also for marine equipment design and navigation. The data used cover the time interval 1999 to 2007 and it was measured on Gloria drilling unit, which operates in the Romanian nearshore of the Black Sea at about 500 meters depth.

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Comparison between M5′ model tree and neural networks for prediction of significant wave height in Lake Superior

Ocean Engineering ◽

10.1016/j.oceaneng.2009.08.008 ◽

2009 ◽

Vol 36 (15-16) ◽

pp. 1175-1181 ◽

Cited By ~ 94

Author(s):

A. Etemad-Shahidi ◽

J. Mahjoobi

Keyword(s):

Neural Networks ◽

Wave Height ◽

Significant Wave Height ◽

Lake Superior ◽

M5 Model Tree ◽

Significant Wave ◽

Model Tree ◽

M5 Model

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Waves off Gopalpur, northern Bay of Bengal during Cyclone Phailin

Annales Geophysicae ◽

10.5194/angeo-32-1073-2014 ◽

2014 ◽

Vol 32 (9) ◽

pp. 1073-1083 ◽

Cited By ~ 19

Author(s):

M. M. Amrutha ◽

V. Sanil Kumar ◽

T. R. Anoop ◽

T. M. Balakrishnan Nair ◽

A. Nherakkol ◽

...

Keyword(s):

Wave Height ◽

Bay Of Bengal ◽

Significant Wave Height ◽

Wave Spectrum ◽

Statistical Parameters ◽

Freak Wave ◽

Maximum Wave Height ◽

Significant Wave ◽

Before And After ◽

Northern Bay Of Bengal

Abstract. The wave statistical parameters during Cyclone Phailin which crossed the northern Bay of Bengal are described based on the Directional Waverider buoy-measured wave data from 8 to 13 October 2013. On 12 October 2013, the cyclone passed within 70 km of the Waverider buoy location with a wind speed of 59.2 m s−1 (115 knots), and during this period, a maximum significant wave height of 7.3 m and a maximum wave height of 13.5 m were measured at 50 m water depth. Eight freak wave events are observed during the study period. The ratio of the maximum wave height to significant wave height recorded is found to be higher than the theoretical value and the ratio of the crest height to wave height during the cyclone was 0.6 to 0.7. The characteristics of the wave spectra before and after the cyclone is studied and found that the high-frequency face of the wave spectrum is proportional to f−3 before the cyclone and is between f−4 and f−5 during the cyclone period.

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