Machine Learning in weakly nonlinear systems: A Case study on Significant wave heights
This work addresses wave height forecasting for oceanic applications, offering incremental improvements in prediction range and accuracy.
This paper tackles the problem of forecasting Significant Wave Heights in oceanic waters using a machine learning method based on the Extra Trees algorithm, achieving a Scatter Index of 0.130 and RMSE of 0.14 for one-day ahead prediction and extending forecasts to 14 days, which is longer than state-of-the-art methods.
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.