scholarly journals Significant Wave Height Prediction with the CRBM-DBN Model

2019 ◽  
Vol 36 (3) ◽  
pp. 333-351 ◽  
Author(s):  
Xining Zhang ◽  
Hao Dai
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Restricted Boltzmann Machine ◽  
Series Data ◽  
Model Parameters ◽  
Boltzmann Machine ◽  
Short Term ◽  
Prediction Ability ◽  
Significant Wave ◽  
Height Prediction

AbstractIn recent years, deep learning technology has been gradually used for time series data prediction in various fields. In this paper, the restricted Boltzmann machine (RBM) in the classical deep belief network (DBN) is substituted with the conditional restricted Boltzmann machine (CRBM) containing temporal information, and the CRBM-DBN model is constructed. Key model parameters, which are determined by the particle swarm optimization (PSO) algorithm, are used to predict the significant wave height. Observed data in 2016, which are from nearshore and offshore buoys (i.e., 42020 and 42001) belonging to the National Data Buoy Center (NDBC), are taken to train the model, and the corresponding data in 2017 are used for testing with lead times of 1–24 h. In addition, we trained the data of 42040 in 2003 and tested the data in 2004 in order to investigate the prediction ability of the CRBM-DBN model for the extreme event. The prediction ability of the model is evaluated by the Nash–Sutcliffe coefficient of efficiency (CE) and root-mean-square error (RMSE). Experiments demonstrate that for the short-term (≤9 h) prediction, the RMSE and CE for the significant wave height prediction are <10 cm and >0.98, respectively. Moreover, the relative error of the short-term prediction for the maximum wave height is less than 26%. The excellent short-term and extreme events forecasting ability of the CRBM-DBN model is vital to ocean engineering applications, especially for designs of ocean structures and vessels.

A hybrid EMD-SVR model for the short-term prediction of significant wave height

2016 ◽  
Vol 124 ◽  
pp. 54-73 ◽  
Author(s):  
W.Y. Duan ◽  
Y. Han ◽  
L.M. Huang ◽  
B.B. Zhao ◽  
M.H. Wang
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Short Term ◽  
Significant Wave ◽  
Term Prediction ◽  
Short Term Prediction

Omnidirectional Return Values for Storm Severity From Directional Extreme Value Models: The Effect of Physical Environment and Sample Size

2014 ◽  
Author(s):  
David Randell ◽  
Elena Zanini ◽  
Michael Vogel ◽  
Kevin Ewans ◽  
Philip Jonathan
Keyword(s):  
Sample Size ◽  
Wave Height ◽  
Significant Wave Height ◽  
Extreme Value ◽  
Dependence Structure ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Directional Model ◽  
Significant Wave ◽  
Extreme Storm

Ewans and Jonathan [2008] shows that characteristics of extreme storm severity in the northern North Sea vary with storm direction. Jonathan et al. [2008] demonstrates, when directional effects are present, that omnidirectional return values should be estimated using a directional extreme value model. Omnidirectional return values so calculated are different in general to those estimated using a model which incorrectly assumes stationarity with respect to direction. The extent of directional variability of extreme storm severity depends on a number of physical factors, including fetch variability. Our ability to assess directional variability of extreme value parameters and return values also improves with increasing sample size in general. In this work, we estimate directional extreme value models for samples of hindcast storm peak significant wave height from locations in ocean basins worldwide, for a range of physical environments, sample sizes and periods of observation. At each location, we compare distributions of omnidirectional 100-year return values estimated using a directional model, to those (incorrectly) estimated assuming stationarity. The directional model for peaks over threshold of storm peak significant wave height is estimated using a non-homogeneous point process model as outlined in Randell et al. [2013]. Directional models for extreme value threshold (using quantile regression), rate of occurrence of threshold exceedances (using a Poisson model), and size of exceedances (using a generalised Pareto model) are estimated. Model parameters are described as smooth functions of direction using periodic B-splines. Parameter estimation is performed using maximum likelihood estimation penalised for parameter roughness. A bootstrap re-sampling procedure, encompassing all inference steps, quantifies uncertainties in, and dependence structure of, parameter estimates and omnidirectional return values.

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.

scholarly journals A fuzzy KNN-based model for significant wave height prediction in large lakes

Oceanologia ◽  
2018 ◽  
Vol 60 (2) ◽  
pp. 153-168 ◽  
Author(s):  
Mohammad Reza Nikoo ◽  
Reza Kerachian ◽  
Mohammad Reza Alizadeh
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Large Lakes ◽  
Significant Wave ◽  
Height Prediction

scholarly journals An EMD-PSO-LSSVM hybrid model for significant wave height prediction

2021 ◽  
Author(s):  
Gang Tang ◽  
Haohao Du ◽  
Xiong Hu ◽  
Yide Wang ◽  
Christophe Claramunt ◽  
...  
Keyword(s):  
Hybrid Model ◽  
Wave Height ◽  
Deep Sea ◽  
Significant Wave Height ◽  
Prediction Accuracy ◽  
Learning Ability ◽  
Support Vector ◽  
Significant Wave ◽  
Height Prediction ◽  
Sea Area

Abstract. Accurate and significant wave height prediction with a couple of hours of warning time should offer major safety improvements for coastal and ocean engineering applications. However, significant wave height phenomenon is nonlinear and nonstationary, which makes any prediction simulation a non straightforward task. The aim of the research presented in this paper is to improve predicted significant wave height via a hybrid algorithm. Firstly, empirical mode decomposition (EMD) is used to preprocess the nonlinear data, which are decomposed into several simple signals. Then, least square support vector machine (LSSVM) with nonlinear learning ability is used to predict the significant wave height, and particle swarm optimization (PSO) is implemented to automatically perform the parameter selection in LSSVM modeling. The EMD-PSO-LSSVM model is used to predict the significant wave height for 1, 3 and 6 hours leading times of two stations in the offshore and deep-sea areas of the North Atlantic Ocean. The results show that the EMD-PSO-LSSVM model can remove the lag in the prediction timing of the single prediction models. Furthermore, the prediction accuracy of the EMD-LSSVM model that has not been optimized in the deep-sea area has been greatly improved, an improvement of the prediction accuracy of Coefficient of determination (R2) from 0.991, 0.982 and 0.959 to 0.993, 0.987 and 0.965, respectively, has been observed. The proposed new hybrid model shows good accuracy and provides an effective way to predict the significant wave height for the deep-sea area.

Applied Data Analytics to Buoy Records for Weather Window Evaluation

2021 ◽  
Author(s):  
Tirtharaj Bhaumik ◽  
Shiladitya Basu
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Data Analytics ◽  
Time Series Data ◽  
Weather Data ◽  
Series Data ◽  
Wave Direction ◽  
Significant Wave ◽  
Using Data ◽  
Future Work

This paper analyzes weather data recorded by typical oceanographic buoys using data analytics and regression techniques. Time series data over a period of more than four decades (1976 – 2020) are reviewed and profiled. A set of key variables including seasonality, wind speed, wind direction, wave period, wave direction, etc., are screened from the buoy measurements to build a predictive model based on multiple linear regression for significant wave height prediction. A sensitivity analysis is then conducted for the available weather window corresponding to specified threshold operational limits of the significant wave height. Key insights are presented along with suggestions for future work to assist marine operators in planning and derisking offshore operations. Utilizing the algorithms and workflows presented in this paper, a user can increase confidence in weather window prediction, and develop safer, efficient offshore operation plans.

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