The Reconstruction of Significant Wave Height Time Series by Using a Neural Network Approach

2004 ◽  
Vol 126 (3) ◽  
pp. 213-219 ◽  
Author(s):  
Felice Arena ◽  
Silvia Puca
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wave Height ◽  
Significant Wave Height ◽  
Error Function ◽  
Neural Network Approach ◽  
Significant Wave ◽  
Time Periods ◽  
Weighted Error ◽  
Storm Model

A Multivariate Neural Network (MNN) algorithm is proposed for the reconstruction of significant wave height time series, without any increase of the error of the MNN output with the number of modelled data. The algorithm uses a weighted error function during the learning phase, to improve the modelling of the higher significant wave height. The ability of the MNN to reconstruct sea storms is tested by applying the equivalent triangular storm model. Finally an application to the NOAA buoys moored off California shows a good performance of the MNN algorithm, both during sea storms and calm time periods.

A New Approach for the Reconstruction of Significant Wave Height Time-Series

2002 ◽  
Author(s):  
Felice Arena ◽  
Silvia Puca ◽  
Brunello Tirozzi
Keyword(s):  
Time Series ◽  
San Francisco ◽  
Wave Height ◽  
Significant Wave Height ◽  
Error Function ◽  
Correlated Data ◽  
New Approach ◽  
Significant Wave ◽  
Storm Model ◽  
Good Agreement

A Neural Network (NN) model is proposed for the reconstruction of significant wave height time series, without any increase of the error of the NN output with the number of reconstructed data. The input of the NN model are correlated data, obtained from nearby stations: no data of the same series we are modelling are used. A weighted error function during the learning phase is also considered to improve the modelling of the higher significant wave height. Furthermore the equivalent triangular storm model is applied to test the ability of the NN model to reconstruct the sea storms. The comparison between actual data of a NOAA buoy moored off San Francisco (California) and the data reconstructed by NN model shows a good agreement, both during calm time periods and during storms.

“Three Sisters” Measured As a Triple Rogue Wave Group

2019 ◽  
Author(s):  
Anne Karin Magnusson ◽  
Karsten Trulsen ◽  
Ole Johan Aarnes ◽  
Elzbieta M. Bitner-Gregersen ◽  
Mika P. Malila
Keyword(s):  
Time Series ◽  
Wave Height ◽  
Significant Wave Height ◽  
Weather Conditions ◽  
Rogue Waves ◽  
Significant Wave ◽  
Wind Sea ◽  
Propagation Methods ◽  
Non Linear Propagation ◽  
Three Sisters

Abstract On November 30, 2018, our attention was caught when analyzing wave profile time series measured by a platform mounted wave sensor (a SAAB REX radar) at Ekofisk, central North Sea. The 20-minute time series had not only one, but three consecutive waves with individual heights that all were more than twice the significant wave height, the two last of them being almost equally high with a factor 2.35 to the significant wave height of 4m (from 4σ(η), over 20 minutes). Counting three rogue waves in one sequence seems to be very rare. In this study we analyze how the shape is evolving in space and time using linear and non-linear propagation methods developed by Mark Donelan [1,2] and Karsten Trulsen [3,4]. Weather conditions and characteristics of the sea state with the ‘Three Sisters’ (named the “Justine Three Sisters”) are presented. It is found that the Three Sisters occurred in a crossing sea condition, with wind sea and swell coming from directions 60 degrees apart, with about same frequency, but very different energy.

scholarly journals Changes of Significant Wave Height, Energy Period and Wave Power in Italy in the Period 1979–2018

2020 ◽  
Vol 2 (1) ◽  
pp. 3
Author(s):  
Tommaso Caloiero ◽  
Francesco Aristodemo ◽  
Danilo Algieri Ferraro
Keyword(s):  
Time Series ◽  
Wave Height ◽  
Significant Wave Height ◽  
Kendall Test ◽  
Wave Power ◽  
Mean Values ◽  
Significant Wave ◽  
Wave Energy Converters ◽  
Long Wave ◽  
Significance Levels

An analysis of a 40-year long wave time series was performed, along the coasts of Italy, in order to identify ongoing trends of two synthetic parameters, significant wave height (Hs) and energy period (Te), and of the wave power (P). First, wave data were deduced from the global atmospheric reanalysis ERA-INTERIM by the ECMWF and checked to verify their consistency. Then, a trend analysis was performed on mean values evaluated at annual and seasonal scales through the non-parametric Mann–Kendall test for three different significance levels equal to 90%, 95% and 99%. The obtained results could be useful for analyses linked to beach morphodynamics and on the identification of field installations of Wave Energy Converters (WECs).

Significant wave height retrieval from Sentinel-1 SAR imagery by convolutional neural network

2020 ◽  
Vol 76 (6) ◽  
pp. 465-477
Author(s):  
Sihan Xue ◽  
Xupu Geng ◽  
Xiao-Hai Yan ◽  
Ting Xie ◽  
Qiuze Yu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Wave Height ◽  
Significant Wave Height ◽  
Significant Wave ◽  
Sar Imagery

Correlation Enhanced Machine Learning Approach based Wave Height Prediction

2018 ◽  
Vol 4 (5) ◽  
pp. 10
Author(s):  
Ruchi Shrivastava ◽  
Dr. Krishna Teerth Chaturvedi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Wave Height ◽  
Significant Wave Height ◽  
Work Performance ◽  
Feature Reduction ◽  
Learning Approaches ◽  
Significant Wave ◽  
Wave Parameters ◽  
Height Prediction

The prediction of wave height is one of the major problems of coastal engineering and coastal structures. In recent years, advances in the prediction of significant wave height have been considerably developed using flexible calculation techniques. In addition to the traditional prediction of significant wave height, soft computing has explored a new way of predicting significant wave heights. This research was conducted in the direction of forecasting a significant wave height using machine learning approaches. In this paper, a problem of significant wave height prediction problem has been tackled by using wave parameters such as wave spectral density. This prediction of significant wave height helps in wave energy converters as well as in ship navigation system. This research will optimize wave parameters for a fast and efficient wave height prediction. For this Pearson’s, Kendall’s and Spearman’s Correlation Coefficients and Particle Swarm Optimization feature reduction techniques are used. So reduced features are taken into consideration for prediction of wave height using neural network. In this work, performance evaluation metrics such as MSE and RMSE values are decreased and gives better performance of classification that is compared with existing research’s implemented methodology. From the experimental results, it is observed that proposed algorithm gives the better prediction as compared to PSO feature reduction technique. So, it is also concluded that Co-relation enhanced neural network is better as compared to PSO based neural network with increased number of features.

Sign in / Sign up

Export Citation Format

Share Document