Distribution-Based Discretisation and Ordinal Classification Applied to Wave Height Prediction

2018 ◽  
pp. 171-179
Author(s):  
David Guijo-Rubio ◽  
Antonio M. Durán-Rosal ◽  
Antonio M. Gómez-Orellana ◽  
Pedro A. Gutiérrez ◽  
César Hervás-Martínez
Keyword(s):  
Wave Height ◽  
Ordinal Classification ◽  
Height Prediction

Evaluating the efficacy of SVMs, BNs, ANNs and ANFIS in wave height prediction

2011 ◽  
Vol 38 (2-3) ◽  
pp. 487-497 ◽  
Author(s):  
Iman Malekmohamadi ◽  
Mohammad Reza Bazargan-Lari ◽  
Reza Kerachian ◽  
Mohammad Reza Nikoo ◽  
Mahsa Fallahnia
Keyword(s):  
Wave Height ◽  
Height Prediction

Wave height prediction in the Western Mediterranean using genetic algorithms

2010 ◽  
Vol 37 (8-9) ◽  
pp. 742-748 ◽  
Author(s):  
B. Cañellas ◽  
S. Balle ◽  
J. Tintoré ◽  
A. Orfila
Keyword(s):  
Genetic Algorithms ◽  
Wave Height ◽  
Western Mediterranean ◽  
Height Prediction

scholarly journals Wave height prediction at the Caspian Sea using a data-driven model and ensemble-based data assimilation methods

2009 ◽  
Vol 11 (2) ◽  
pp. 154-164 ◽  
Author(s):  
Ahmadreza Zamani ◽  
Ahmadreza Azimian ◽  
Arnold Heemink ◽  
Dimitri Solomatine
Keyword(s):  
Data Assimilation ◽  
Wave Height ◽  
Caspian Sea ◽  
State Vector ◽  
Coefficient Of Performance ◽  
Propagation Mechanism ◽  
System State ◽  
Extended State ◽  
The Caspian Sea ◽  
Height Prediction

There are successful experiences with the application of ANN and ensemble-based data assimilation methods in the field of flood forecasting and estuary flow. In the present work, the combination of dynamic Artificial Neural Network and Ensemble Kalman Filter (EnKF) is applied on wind-wave data. ANN is used for the time propagation mechanism that governs the time evolution of the system state. The system state consists of the significant wave height that is affected by wind speed and wind direction. The relevant inputs are selected by analysing the Average Mutual Information. By help of the observations, the EnKF will correct the output of the ANN to find the best estimate of the wave height. A combination of ANN with EnKF acts as an output correction scheme. To deal with the time-delayed states, the extended state vector is taken and the dynamic equation of the extended state vector is used in EnKF. Application of the proposed scheme is examined by using five-month hourly buoy measurement at the Caspian Sea and several model runs with different assimilation–forecast cycles.The coefficient of performance and root mean square error are used to access performance of the method.

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.

Wave Height Prediction in Unbounded Coastal Domains with Bathymetric Discontinuity

1991 ◽  
Vol 34 (2) ◽  
pp. 145-158 ◽  
Author(s):  
Shigeaki Tsutsui ◽  
Don P. Lewis
Keyword(s):  
Wave Height ◽  
Height Prediction
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