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

Rana Muhammad Adnan Ikram, Xinyi Cao (), Kulwinder Singh Parmar, Ozgur Kisi (), Shamsuddin Shahid and Mohammad Zounemat-Kermani
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Rana Muhammad Adnan Ikram: School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China
Xinyi Cao: College of Environmental Sciences, Sichuan Agricultural University, Chengdu 611130, China
Kulwinder Singh Parmar: Department of Mathematical Sciences, IKG Punjab Technical University, Jalandhar 144603, India
Ozgur Kisi: Department of Civil Engineering, Lübeck University of Applied Science, 23562 Lübeck, Germany
Shamsuddin Shahid: School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia
Mohammad Zounemat-Kermani: Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman 76169-14111, Iran

Mathematics, 2023, vol. 11, issue 14, 1-24

Abstract: 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 (R 2 ) 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.

Keywords: significant wave height; short-term prediction; Gaussian process regression; partial least square regression (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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