A novel hybrid model based on STL decomposition and one-dimensional convolutional neural networks with positional encoding for significant wave height forecast

2021 ◽  
Vol 173 ◽  
pp. 531-543
Author(s):  
Shaobo Yang ◽  
Zegui Deng ◽  
Xingfei Li ◽  
Chongwei Zheng ◽  
Lintong Xi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hybrid Model ◽  
Wave Height ◽  
Significant Wave Height ◽  
One Dimensional ◽  
Significant Wave ◽  
Model Based

Neural Networks Based Simulation of Significant Wave Height

2007 ◽  
Author(s):  
H. Bazargan ◽  
H. Bahai ◽  
A. Aminzadeh-Gohari ◽  
A. Bazargan
Keyword(s):  
Neural Networks ◽  
Wave Height ◽  
Significant Wave Height ◽  
Optimization Technique ◽  
Conditional Probability Density ◽  
Significant Wave ◽  
The Pacific ◽  
Wave Heights ◽  
Significant Wave Heights ◽  
Long Term Forecasting

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.

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

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.

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.

Significant wave height record extension by neural networks and reanalysis wind data

2015 ◽  
Vol 94 ◽  
pp. 128-140 ◽  
Author(s):  
D.J. Peres ◽  
C. Iuppa ◽  
L. Cavallaro ◽  
A. Cancelliere ◽  
E. Foti
Keyword(s):  
Neural Networks ◽  
Wave Height ◽  
Significant Wave Height ◽  
Wind Data ◽  
Significant Wave

scholarly journals A Dynamic Stochastic Hybrid Model to Represent Significant Wave Height and Wave Period for Marine Energy Representation

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 887
Author(s):  
Humberto Verdejo ◽  
Almendra Awerkin ◽  
Wolfgang Kliemann ◽  
Cristhian Becker ◽  
Héctor Chávez ◽  
...  
Keyword(s):  
Power Generation ◽  
Hybrid Model ◽  
Wave Height ◽  
Significant Wave Height ◽  
Wave Period ◽  
Estimation Methods ◽  
Wave Power ◽  
Significant Wave ◽  
Dynamic Stochastic ◽  
Wave Power Generation

This paper presents a methodology to represent ocean wave power generation based on real data observation for significant wave height (SWH or H s ) and wave period (WP or T). This technique is based on a hybrid model, which considers Fourier series and stochastic differential equations, allowing a continuous time representation of the random changes in the parameters associated with wave power generation ( H s and T). The methodology is explained, including estimation methods and a validation procedure. The data series generated by the models erre used to create simulated wave power output applying a transformed matrix and a theoretical model. The results validate the utilization of this technique, when the objective is to obtain a robust dynamic representation of a random process, oriented to linear studies.

Real-time significant wave height estimation from raw ocean images based on 2D and 3D deep neural networks

2020 ◽  
Vol 201 ◽  
pp. 107129 ◽  
Author(s):  
Heejeong Choi ◽  
Minsik Park ◽  
Gyubin Son ◽  
Jaeyun Jeong ◽  
Jaesun Park ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Wave Height ◽  
Significant Wave Height ◽  
Deep Neural Networks ◽  
Height Estimation ◽  
Significant Wave ◽  
2D And 3D

scholarly journals A hybrid model based on convolutional neural networks and fuzzy kernel K-medoids for lung cancer detection

2021 ◽  
Vol 24 (1) ◽  
pp. 126
Author(s):  
Glori Stephani Saragih ◽  
Zuherman Rustam ◽  
Jane Eva Aurelia
Keyword(s):  
Lung Cancer ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hybrid Model ◽  
Cancer Detection ◽  
High Performance ◽  
Correct Diagnosis ◽  
Computational Time ◽  
Model Based ◽  
Lung Cancer Detection

Lung cancer is the deadliest cancer worldwide. Correct diagnosis of lung cancer is one of the main tasks that is challenging tasks, so the patient can be treated as soon as possible. In this research, we proposed a hybrid model based on convolutional neural networks (CNN) and fuzzy kernel k-medoids (FKKM) for lung cancer detection, where the magnetic resonance imaging (MRI) images are transmitted to CNN, and then the output is used as new input for FKKM. The dataset used in this research consist of MRI images taken from someone who had lung cancer with the treatment of anti programmed cell death-1 (anti-PD1) immunotherapy in 2016 that obtained from the cancer imaging archive. The proposed method obtained accuracy, sensitivity, precision, specificity, and F1-score 100% by using radial basis function (RBF) kernel with sigma of {10<sub>­</sub>­<sup>-8</sup>, 10<sub>­</sub>­<sup>-4</sup>, 10<sub>­</sub>­<sup>-3</sup>, 5x10<sub>­</sub>­<sup>-2</sup>, 10<sub>­</sub>­<sup>-1</sup>, 1, 10­­<sup>4</sup>} in 20-fold cross-validation. The computational time is only taking less than 10 seconds to forward dataset to CNN and 3.85 ± 0.6 seconds in FKKM model. So, the proposed method is more efficient in time and has a high performance for detecting lung cancer from MRI images.

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