ANN-assisted prediction of wave run-up around a tension leg platform under irregular wave conditions

This study proposes a novel prediction method for nonlinear wave run-ups around a TLP by combining the linear diffraction method with deep learning techniques. Initially, the linear diffraction method evaluates relative wave motion around the platform based on measured incident waves and frequency domain analysis. Subsequently, an artificial neural network (ANN) predicts the peak values of nonlinear wave run-ups, utilizing incident wave data and linear calculation results as inputs. Two different ANN models were proposed, each tailored to specific output variables. The first, the ζ-based ANN model, predicts the peak value of wave run-ups, while the second, the α-based ANN model, predicts the nonlinear amplification factor. The incorporation of a neural network significantly enhances the prediction accuracy for the peak values of wave run-ups. Moreover, the α-based ANN model demonstrates superior accuracy in predicting high run-up events and better generalization ability across various wave conditions compared to the ζ-based ANN model. The study also investigates the effect of various preprocessing methods on prediction performance. The over-sampling method improves accuracy for high run-up events but degrades predictions for low run-up regions, while the polynomial feature transformation reduces prediction errors for high run-up in the ζ-based ANN model. •Two ANN models for predicting wave run-ups were proposed by integrating with the linear diffraction method.•The α-based ANN model based on nonlinear wave amplification demonstrates superior accuracy in predicting high run-up events.•The ANN models improved the prediction for the peak values of wave run-ups, reducing the RMSE by up to 30% for overall cases.•The study investigated the effect of various preprocessing methods on prediction performance for wave run-up.