Predictive capabilities of data-driven machine learning techniques on wave-bridge interactions

Zhu, Deming; Zhang, Jiaxin; Qian, Wei; Dong, You; Bastidas‐Arteaga, Emilio

doi:10.1016/j.apor.2023.103597

Cited by 4 publications

(1 citation statement)

References 43 publications

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“…In a relevant study, Pourzangbar et al [13] demonstrated that the prediction of scour depth due to non-breaking waves with the aid of Machine Learning (ML) models such as regression trees and support vector regression achieves the highest accuracy. Therefore, soft computing and ML methods, which fall under artificial intelligence methodologies, have been successfully utilized in modeling wave characteristics and wave heights [14][15][16]. Besides the beneficial features of the ML methodologies, it should be mentioned that the majority of ML models do not offer a simple, explicit description of the mathematical structure of the constructed models.…”

Section: Introductionmentioning

confidence: 99%

Modeling Significant Wave Heights for Multiple Time Horizons Using Metaheuristic Regression Methods

et al. 2023

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The study examines the applicability of six metaheuristic regression techniques—M5 model tree (M5RT), multivariate adaptive regression spline (MARS), principal component regression (PCR), random forest (RF), partial least square regression (PLSR) and Gaussian process regression (GPR)—for predicting short-term significant wave heights from one hour to one day ahead. Hourly data from two stations, Townsville and Brisbane Buoys, Queensland, Australia, and historical values were used as model inputs for the predictions. The methods were assessed based on root mean square error, mean absolute error, determination coefficient and new graphical inspection methods (e.g., Taylor and violin charts). On the basis of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) statistics, it was observed that GPR provided the best accuracy in predicting short-term single-time-step and multi-time-step significant wave heights. On the basis of mean RMSE, GPR improved the accuracy of M5RT, MARS, PCR, RF and PLSR by 16.63, 8.03, 10.34, 3.25 and 7.78% (first station) and by 14.04, 8.35, 13.34, 3.87 and 8.30% (second station) for the test stage.

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