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