# Significant Wave Height Prediction based on Wavelet Graph Neural Network

arXiv: https://arxiv.org/abs/2107.09483 · HTML: https://mlsanity.com/paper/2107.09483/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [Temporal Pattern Attention for Multivariate Time Series Forecasting](https://mlsanity.com/paper/1809.04206/) (2018)

## Built on by (most cited first, 4 of 4)

- [Graph Neural Network for spatiotemporal data: methods and applications](https://mlsanity.com/paper/2306.00012/) (2023)
- [Exceedance Probability Forecasting via Regression for Significant Wave Height Prediction](https://mlsanity.com/paper/2206.09821/) (2022)
- [A Simple Baseline for Adversarial Domain Adaptation-based Unsupervised Flood Forecasting](https://mlsanity.com/paper/2206.08105/) (2022)
- [On the Limits of Univariate Deep Learning for Significant Wave Height Forecasting](https://mlsanity.com/paper/2609.30688/) (2026)

## Abstract (arXiv)

Computational intelligence-based ocean characteristics forecasting applications, such as Significant Wave Height (SWH) prediction, are crucial for avoiding social and economic loss in coastal cities. Compared to the traditional empirical-based or numerical-based forecasting models, "soft computing" approaches, including machine learning and deep learning models, have shown numerous success in recent years. In this paper, we focus on enabling the deep learning model to learn both short-term and long-term spatial-temporal dependencies for SWH prediction. A Wavelet Graph Neural Network (WGNN) approach is proposed to integrate the advantages of wavelet transform and graph neural network. Several parallel graph neural networks are separately trained on wavelet decomposed data, and the reconstruction of each model's prediction forms the final SWH prediction. Experimental results show that the proposed WGNN approach outperforms other models, including the numerical models, the machine learning models, and several deep learning models.