Machine Learning in weakly nonlinear systems: A Case study on Significant wave heights

TL;DR

This study introduces an Extra Trees machine learning approach for forecasting significant wave heights up to 14 days ahead, outperforming existing methods and enabling longer-term oceanic wave predictions.

Contribution

The paper presents a novel machine learning framework that extends wave height forecasting to 14 days, surpassing the typical 120-hour limit of previous models.

Findings

Achieved low Scatter Index and high correlation for 1-day and 14-day forecasts.

Outperformed state-of-the-art methods in wave height prediction accuracy.

Extended reliable wave forecasting period from 5 days to 14 days.

Abstract

This paper proposes a machine learning method based on the Extra Trees (ET) algorithm for forecasting Significant Wave Heights in oceanic waters. To derive multiple features from the CDIP buoys, which make point measurements, we first nowcast various parameters and then forecast them at 30-min intervals. The proposed algorithm has Scatter Index (SI), Bias, Correlation Coefficient, Root Mean Squared Error (RMSE) of 0.130, -0.002, 0.97, and 0.14, respectively, for one day ahead prediction and 0.110, -0.001, 0.98, and 0.122, respectively, for 14-day ahead prediction on the testing dataset. While other state-of-the-art methods can only forecast up to 120 hours ahead, we extend it further to 14 days. Our proposed setup includes spectral features, hv-block cross-validation, and stringent QC criteria. The proposed algorithm performs significantly better than the state-of-the-art methods commonly used for significant wave height forecasting for one-day ahead prediction. Moreover, the improved performance of the proposed machine learning method compared to the numerical methods shows that this performance can be extended to even longer periods allowing for early prediction of significant wave heights in oceanic waters.