ICCE 2022

How to Cite

AUTOMATIC WAVE MODEL CALIBRATION USING SURROGATE MODELS. (2023). Coastal Engineering Proceedings, 37, waves.43.


Alonso and Solari (2017) presented an approach for the automatic calibration of a third-generation wave model, using the significant wave height error as the objective function. Alonso and Solari (2021) extended the methodology, incorporating a spectral error as the objective function and the use of the maximum dissimilarity algorithm to minimize the number of sea states used for calibration without losing representativeness. It was found that the use of the spectral error does not necessarily guarantee an improvement in Hs results. Furthermore, calculation times were still too long for the general application of the method. The objective of this work is twofold: (1) to introduce the use of surrogate models in the automatic calibration algorithm to speed up computation times, and (2) to explore a wide spectrum of objective functions for model calibration.


Alonso, Solari (2017): Automatic calibration of a wave model with an evolutionary Bayesian method. Coastal Engineering Proceedings, vol. 1, p. 35.

Alonso, Solari (2021): Automatic calibration and uncertainty quantification in waves dynamical downscaling, Coastal Engineering, ELSEVIER, vol. 169.

Rasmussen, Williams (2006): Gaussian Processes for Machine Learning. MIT Press, 272 pp.

Vrugt, Beven (2018): Embracing equifinality with efficiency : limits of Acceptability sampling using the DREAM (LOA) algorithm. J. Hydrol. ELSEVIER Vol. 559, 954–971.

Zhou, Su, Cui (2018): An adaptive Kriging surrogate method for efficient joint estimation of hydraulic and biochemical parameters in reactive transport modeling. Journal of Contaminant Hydrology, ELSEVIER, vol. 216, 50–57.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright (c) 2023 Sebastián Solari, Rodrigo Alonso