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

Computational intelligence-based ocean characteristics forecastingapplications, such as Significant Wave Height (SWH) prediction, are crucial foravoiding social and economic loss in coastal cities. Compared to thetraditional empirical-based or numerical-based forecasting models, "softcomputing" approaches, including machine learning and deep learning models,have shown numerous success in recent years. In this paper, we focus onenabling the deep learning model to learn both short-term and long-termspatial-temporal dependencies for SWH prediction. A Wavelet Graph NeuralNetwork (WGNN) approach is proposed to integrate the advantages of wavelettransform and graph neural network. Several parallel graph neural networks areseparately trained on wavelet decomposed data, and the reconstruction of eachmodel's prediction forms the final SWH prediction. Experimental results showthat the proposed WGNN approach outperforms other models, including thenumerical models, the machine learning models, and several deep learningmodels.

Significant Wave Height Prediction based on Wavelet Graph Neural Network