Multivariate, Multistep Forecasting, Reconstruction and Feature Selection of Ocean Waves via Recurrent and Sequence-to-Sequence Networks

Abstract

This article explores the concepts of ocean wave multivariate multistep forecasting, reconstruction and feature selection. We introduce recurrent neural network frameworks, integrated with Bayesian hyperparameter optimization and Elastic Net methods. We consider both short- and long-term forecasts and reconstruction, for significant wave height and output power of the ocean waves. Sequence-to-sequence neural networks are being developed for the first time to reconstruct the missing characteristics of ocean waves based on information from nearby wave sensors. Our results indicate that the Adam and AMSGrad optimization algorithms are the most robust ones to optimize the sequence-to-sequence network. For the case of significant wave height reconstruction, we compare the proposed methods with alternatives on a well-studied dataset. We show the superiority of the proposed methods considering several error metrics. We design a new case study based on measurement stations along the east coast of the United States and investigate the feature selection concept. Comparisons substantiate the benefit of utilizing Elastic Net. Moreover, case study results indicate that when the number of features is considerable, having deeper structures improves the performance.

References in corpus (11)

• Adam: A Method for Stochastic Optimization
• Sequence to Sequence Learning with Neural Networks
• Practical Bayesian Optimization of Machine Learning Algorithms
• A Critical Review of Recurrent Neural Networks for Sequence Learning
• Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
• On the Convergence of Adam and Beyond
• Multi-Task Feature Learning Via Efficient l2,1-Norm Minimization
• Professor Forcing: A New Algorithm for Training Recurrent Networks
• Feature Engineering and Forecasting via Derivative-free Optimization and Ensemble of Sequence-to-sequence Networks with Applications in Renewable Energy
• Stochastic analysis of ocean wave states with and without rogue waves
• Text-Independent Speaker Verification Using Long Short-Term Memory Networks

Cited by in corpus (1)

• Feature Engineering and Forecasting via Derivative-free Optimization and Ensemble of Sequence-to-sequence Networks with Applications in Renewable Energy