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.

scholarly journals Application of neural networks and support vector machine for significant wave height prediction

Oceanologia ◽  
2017 ◽  
Vol 59 (3) ◽  
pp. 331-349 ◽  
Author(s):  
Jadran Berbić ◽  
Eva Ocvirk ◽  
Dalibor Carević ◽  
Goran Lončar
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Wave Height ◽  
Significant Wave Height ◽  
Support Vector ◽  
Significant Wave ◽  
Height Prediction

Gap-Filling and Predicting Wave Parameters Using Support Vector Regression Method

2011 ◽  
Author(s):  
Maziar Golestani ◽  
Mostafa Zeinoddini
Keyword(s):  
Support Vector Regression ◽  
Wave Height ◽  
Significant Wave Height ◽  
Rational Design ◽  
Wave Spectrum ◽  
Support Vector ◽  
Wave Spectra ◽  
Significant Wave ◽  
Wave Parameters ◽  
Data Gaps

Knowledge of relevant oceanographic parameters is of utmost importance in the rational design of coastal structures and ports. Therefore, an accurate prediction of wave parameters is especially important for safety and economic reasons. Recently, statistical learning methods, such as Support Vector Regression (SVR) have been successfully employed by researchers in problems such as lake water level predictions, and significant wave height prediction. The current study reports potential application of a SVR approach to predict the wave spectra and significant wave height. Also the capability of the model to fill data gaps was tested using different approaches. Concurrent wind and wave records (standard meteorological and spectral density data) from 4 stations in 2003, 2007, 2008 and 2009 were used both for the training the SVR system and its verification. The choice of these four locations facilitated the comparison of model performances in different geographical areas. The SVR model was then used to obtain predictions for the wave spectra and also time series of wave parameters (separately for each station) such as its Hs and Tp from spectra and wind records. New approach was used to predict wave spectra comparing to similar studies. Reasonably well correlation was found between the predicted and measured wave parameters. The SVR model was first trained and tested using various methods for selecting training data. Also different values for SVM parameters (e.g. tolerance of termination criterion, cost, and gamma in kernel function) were tested. The best possible results were obtained using a Unix shell script (in Linux) which automatically implements different values for different input parameters and finds the best regression by calculating statistical scores like correlation of coefficient, RMSE, bias and scatter index. Finally for a better understanding of the results, Quantile-Quantile plots were produced. The results show that SVR can be successfully used for prediction of Hs and wave spectrum out of a series of wind and spectral wave parameters inputs. Also it was noticed that SVR is an efficient tool to be used when data gaps are present in the data.

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.

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 Using Adjacent Buoy Information to Predict Wave Heights of Typhoons Offshore of Northeastern Taiwan

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1800 ◽  
Author(s):  
Chih-Chiang Wei ◽  
Chia-Jung Hsieh
Keyword(s):  
Prediction Model ◽  
Wave Height ◽  
Prediction Accuracy ◽  
Complete Information ◽  
Rogue Waves ◽  
Superior Performance ◽  
Efficiency Coefficient ◽  
Height Prediction ◽  
Relative Errors ◽  
Sea Area

In the northeastern sea area of Taiwan, typhoon-induced long waves often cause rogue waves that endanger human lives. Therefore, having the ability to predict wave height during the typhoon period is critical. The Central Weather Bureau maintains the Longdong and Guishandao buoys in the northeastern sea area of Taiwan to conduct long-term monitoring and collect oceanographic data. However, records have often become lost and the buoys have suffered other malfunctions, causing a lack of complete information concerning wind-generated waves. The goal of the present study was to determine the feasibility of using information collected from the adjacent buoy to predict waves. In addition, the effects of various factors such as the path of a typhoon on the prediction accuracy of data from both buoys are discussed herein. This study established a prediction model, and two scenarios were used to assess the performance: Scenario 1 included information from the adjacent buoy and Scenario 2 did not. An artificial neural network was used to establish the wave height prediction model. The research results demonstrated that (1) Scenario 1 achieved superior performance with respect to absolute errors, relative errors, and efficiency coefficient (CE) compared with Scenario 2; (2) the CE of Longdong (0.802) was higher than that of Guishandao (0.565); and (3) various types of typhoon paths were observed by examining each typhoon. The present study successfully determined the feasibility of using information from the adjacent buoy to predict waves. In addition, the effects of various factors such as the path of a typhoon on the prediction accuracy of both buoys were also discussed.

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