English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This article explores the concepts of ocean wave multivariate multistepforecasting, reconstruction and feature selection. We introduce recurrentneural network frameworks, integrated with Bayesian hyperparameter optimizationand Elastic Net methods. We consider both short- and long-term forecasts andreconstruction, for significant wave height and output power of the oceanwaves. Sequence-to-sequence neural networks are being developed for the firsttime to reconstruct the missing characteristics of ocean waves based oninformation from nearby wave sensors. Our results indicate that the Adam andAMSGrad optimization algorithms are the most robust ones to optimize thesequence-to-sequence network. For the case of significant wave heightreconstruction, we compare the proposed methods with alternatives on awell-studied dataset. We show the superiority of the proposed methodsconsidering several error metrics. We design a new case study based onmeasurement stations along the east coast of the United States and investigatethe feature selection concept. Comparisons substantiate the benefit ofutilizing Elastic Net. Moreover, case study results indicate that when thenumber of features is considerable, having deeper structures improves theperformance.

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