An Evolutionary Artificial Neural Network approach for spatio-temporal wave height time series reconstruction

[1]  Yuge Han,et al.  Global temperature reconstruction of equipment based on the local temperature image using TRe-GAN , 2022, Appl. Soft Comput..

[2]  Dongkai Yang,et al.  Retrieval and Assessment of Significant Wave Height from CYGNSS Mission Using Neural Network , 2022, Remote. Sens..

[3]  Y. Liu,et al.  SAITS: Self-Attention-based Imputation for Time Series , 2022, Expert Syst. Appl..

[4]  Cordula Berkenbrink,et al.  Prediction and reconstruction of ocean wave heights based on bathymetric data using LSTM neural networks , 2021, Ocean Engineering.

[5]  L. Aouf,et al.  The Wide Swath Significant Wave Height: An Innovative Reconstruction of Significant Wave Heights From CFOSAT’s SWIM and Scatterometer Using Deep Learning , 2021, Geophysical Research Letters.

[6]  Javier Del Ser,et al.  Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives , 2021, Appl. Soft Comput..

[7]  F. Taveira-Pinto,et al.  Integrated study of triboelectric nanogenerator for ocean wave energy harvesting: Performance assessment in realistic sea conditions , 2021, Nano Energy.

[8]  Pedro Antonio Gutiérrez,et al.  Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux , 2021, Energies.

[9]  Ramin Ramezani,et al.  Operational limits for aquaculture operations from a risk and safety perspective , 2020, Reliab. Eng. Syst. Saf..

[10]  Sancho Salcedo-Sanz,et al.  k-Gaps: a novel technique for clustering incomplete climatological time series , 2020, Theoretical and Applied Climatology.

[11]  Rita P. Ribeiro,et al.  Imbalanced regression and extreme value prediction , 2020, Machine Learning.

[12]  N. Guillou Estimating wave energy flux from significant wave height and peak period , 2020 .

[13]  Lawrence V. Snyder,et al.  Forecasting, hindcasting and feature selection of ocean waves via recurrent and sequence-to-sequence networks , 2020 .

[14]  Gabriela F. Nane,et al.  Statistical models for improving significant wave height predictions in offshore operations , 2020, Ocean Engineering.

[15]  F. Taveira-Pinto,et al.  Marine renewable energy , 2020, Renewable Energy.

[16]  J. Parunov,et al.  Uncertainties of Estimating Extreme Significant Wave Height for Engineering Applications Depending on the Approach and Fitting Technique—Adriatic Sea Case Study , 2020, Journal of Marine Science and Engineering.

[17]  Jaehun Park,et al.  Reconstruction of Sea Level Data around the Korean Coast Using Artificial Neural Network Methods , 2020, Journal of Coastal Research.

[18]  Amin Masoumi,et al.  Application of neural network and weighted improved PSO for uncertainty modeling and optimal allocating of renewable energies along with battery energy storage , 2020, Appl. Soft Comput..

[19]  Edvard Tijan,et al.  Big Data Management in Maritime Transport , 2019, Journal of Maritime & Transportation Science.

[20]  T. Caloiero,et al.  Trend analysis of significant wave height and energy period in southern Italy , 2019, Theoretical and Applied Climatology.

[21]  Xin-She Yang,et al.  Bio-inspired computation: Where we stand and what's next , 2019, Swarm Evol. Comput..

[22]  Gunnar Rätsch,et al.  GP-VAE: Deep Probabilistic Time Series Imputation , 2019, AISTATS.

[23]  S. Dong,et al.  Wave energy assessment based on trivariate distribution of significant wave height, mean period and direction , 2019, Applied Ocean Research.

[24]  M. A. Mustapha,et al.  Influence of Oceanographic Parameters on the Seasonal Potential Fishing Grounds of Rastrelliger kanagurta using Maximum Entropy Models and Remotely Sensed Data , 2019, Sains Malaysiana.

[25]  Luigi Cavaleri,et al.  Large waves and drifting buoys in the Southern Ocean , 2019, Ocean Engineering.

[26]  Cheng Li,et al.  A BP neural network model optimized by Mind Evolutionary Algorithm for predicting the ocean wave heights , 2018, Ocean Engineering.

[27]  Chen Chen Case study on wave-current interaction and its effects on ship navigation , 2018, Journal of Hydrodynamics.

[28]  Lei Li,et al.  BRITS: Bidirectional Recurrent Imputation for Time Series , 2018, NeurIPS.

[29]  Francisco Fernández-Navarro,et al.  Global Sensitivity Estimates for Neural Network Classifiers , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[30]  Mario Motta,et al.  Temperature sensor signal reconstruction for failure detection of vapor compression system , 2017, Appl. Soft Comput..

[31]  Sancho Salcedo-Sanz,et al.  Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm – Extreme Learning Machine approach , 2016 .

[32]  Erik Vanem,et al.  Joint statistical models for significant wave height and wave period in a changing climate , 2016 .

[33]  G. S. Dwarakish,et al.  Real-time prediction of waves using neural networks trained by particle swarm optimization , 2016 .

[34]  Yan Liu,et al.  Recurrent Neural Networks for Multivariate Time Series with Missing Values , 2016, Scientific Reports.

[35]  César Hervás-Martínez,et al.  Massive missing data reconstruction in ocean buoys with evolutionary product unit neural networks , 2016 .

[36]  Sancho Salcedo-Sanz,et al.  A hybrid genetic algorithm—extreme learning machine approach for accurate significant wave height reconstruction , 2015 .

[37]  Esther-Lydia Silva-Ramírez,et al.  Single imputation with multilayer perceptron and multiple imputation combining multilayer perceptron and k-nearest neighbours for monotone patterns , 2015, Appl. Soft Comput..

[38]  Chong-wei Zheng,et al.  Variation of the wave energy and significant wave height in the China Sea and adjacent waters , 2015 .

[39]  José Luis Rojo-Álvarez,et al.  Support vector machines in engineering: an overview , 2014, WIREs Data Mining Knowl. Discov..

[40]  Hui Li,et al.  Evolutionary artificial neural networks: a review , 2011, Artificial Intelligence Review.

[41]  Neil D. Lawrence,et al.  Overlapping Mixtures of Gaussian Processes for the Data Association Problem , 2011, Pattern Recognit..

[42]  Pedro Antonio Gutiérrez,et al.  Combined projection and kernel basis functions for classification in evolutionary neural networks , 2009, Neurocomputing.

[43]  Pedro Antonio Gutiérrez,et al.  Evolutionary product-unit neural networks classifiers , 2008, Neurocomputing.

[44]  Mia Hubert,et al.  An adjusted boxplot for skewed distributions , 2008, Comput. Stat. Data Anal..

[45]  Lorenzo Rosasco,et al.  Elastic-net regularization in learning theory , 2008, J. Complex..

[46]  G. Casella,et al.  The Bayesian Lasso , 2008 .

[47]  Oleg Makarynskyy,et al.  Wave Prediction and Data Supplementation with Artificial Neural Networks , 2007 .

[48]  César Hervás-Martínez,et al.  Evolutionary product unit based neural networks for regression , 2006, Neural Networks.

[49]  A. C. Martínez-Estudillo,et al.  Hybridization of evolutionary algorithms and local search by means of a clustering method , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[50]  M. Hubert,et al.  A Robust Measure of Skewness , 2004 .

[51]  Josep R. Medina,et al.  Discussion of "Predictions of Missing Wave Data by Recurrent Neuronets" , 2004 .

[52]  L. Breiman Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[53]  Christos Stefanakos,et al.  A unified methodology for the analysis, completion and simulation of nonstationary time series with missing values, with application to wave data , 2001 .

[54]  C. Guedes Soares,et al.  On the choice of data transformation for modelling time series of significant wave height , 1999 .

[55]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[56]  Wilton Sturges,et al.  On interpolating gappy records for time‐series analysis , 1983 .

[57]  Rory O. R. Y. Thompson,et al.  Spectral Estimation from Irregularly Spaced Data , 1971 .

[58]  Yoojeong Noh,et al.  Data gap analysis of ship and maritime data using meta learning , 2021, Appl. Soft Comput..

[59]  Tommy S. W. Wong,et al.  Information recovery from measured data by linear artificial neural networks - An example from rainfall-runoff modeling , 2011, Appl. Soft Comput..

[60]  Brunello Tirozzi,et al.  Neural Network Approach to the Problem of Recovering Lost Data In a Network of Marine Buoys , 2001 .