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Publications
Yanchin, I. and Guedes Soares, C. (2024), Gradient boosted trees and denoising autoencoder to correct numerical wave forecasts, Journal of Marine Science and Engineering, Vol. 12, 1573
The paper is dedicated to correcting the WAM/ICON numerical wave model predictions by reducing the residue between the model's predictions and the actual buoy observations. The two parameters used in this paper are significant wave height and wind speed. The paper proposes two machine-learning models to solve this task. Both models are multioutput models and correct the significant wave height and wind speed simultaneously. The first machine-learning model is based on Gradient Boosted Trees, which are trained to predict the residue between the model's forecasts and the actual buoy observations using the other parameters predicted by the numerical model as inputs. The paper demonstrates that this model can significantly reduce errors for all used geographical locations. The paper also uses SHapley Additive exPlanation values to investigate the influence the numerically predicted wave parameters have when the machine-learning model predicts the residue. To design the second model, it is assumed that the residue can be modelled as noise added to the actual values. Therefore, the paper proposes to use Denoising Autoencoder to remove this noise from the numerical model's prediction. The results demonstrate that Denoising Autoencoders can remove the noise for the wind speed parameter, but their performance is poor for the significant wave height. The paper provides some explanations of why this may happen.
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