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

Author

Listed:
  • 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
    Department of Civil Engineering, School of Technology, Ilia State University, 0162 Tbilisi, Georgia
    School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia)

  • 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)

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.

Suggested Citation

  • Rana Muhammad Adnan Ikram & Xinyi Cao & Kulwinder Singh Parmar & Ozgur Kisi & Shamsuddin Shahid & Mohammad Zounemat-Kermani, 2023. "Modeling Significant Wave Heights for Multiple Time Horizons Using Metaheuristic Regression Methods," Mathematics, MDPI, vol. 11(14), pages 1-24, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3141-:d:1195367
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    References listed on IDEAS

    as
    1. Rana Muhammad Adnan Ikram & Leonardo Goliatt & Ozgur Kisi & Slavisa Trajkovic & Shamsuddin Shahid, 2022. "Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction," Mathematics, MDPI, vol. 10(16), pages 1-30, August.
    2. Huang, Weinan & Dong, Sheng, 2021. "Improved short-term prediction of significant wave height by decomposing deterministic and stochastic components," Renewable Energy, Elsevier, vol. 177(C), pages 743-758.
    3. Solomon Asante-Okyere & Chuanbo Shen & Yao Yevenyo Ziggah & Mercy Moses Rulegeya & Xiangfeng Zhu, 2018. "Investigating the Predictive Performance of Gaussian Process Regression in Evaluating Reservoir Porosity and Permeability," Energies, MDPI, vol. 11(12), pages 1-13, November.
    4. Rana Muhammad Adnan & Kulwinder Singh Parmar & Salim Heddam & Shamsuddin Shahid & Ozgur Kisi, 2021. "Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    5. Cornejo-Bueno, L. & Nieto-Borge, J.C. & García-Díaz, P. & Rodríguez, G. & Salcedo-Sanz, S., 2016. "Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm – Extreme Learning Machine approach," Renewable Energy, Elsevier, vol. 97(C), pages 380-389.
    6. Ali, Mumtaz & Prasad, Ramendra, 2019. "Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 281-295.
    7. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    8. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    9. Rana Muhammad Adnan & Sarita Gajbhiye Meshram & Reham R. Mostafa & Abu Reza Md. Towfiqul Islam & S. I. Abba & Francis Andorful & Zhihuan Chen, 2023. "Application of Advanced Optimized Soft Computing Models for Atmospheric Variable Forecasting," Mathematics, MDPI, vol. 11(5), pages 1-29, March.
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