A hybrid genetic algorithm-extreme learning machine approach for accurate significant wave height reconstruction

作者：Alexandre E^*; Cuadra L; Nieto Borge J C; Candil Garcia G; del Pino M; Salcedo Sanz S

来源：Ocean Modelling, 2015, 92: 115-123.

DOI：10.1016/j.ocemad.2015.06.010

• Significant wave height local reconstruction
• Extreme learning machines
• Support vector regression
• Gaussian process regression
• Genetic algorithm

Wave parameters computed from time series measured by buoys (significant wave height H-s, mean wave period, etc.) play a key role in coastal engineering and in the design and operation of wave energy converters. Storms or navigation accidents can make measuring buoys break down, leading to missing data gaps. In this paper we tackle the problem of locally reconstructing H-s at out buoys by using wave parameters from nearby buoys, based on the spatial correlation among values at neighboring buoy locations. The novelty of our approach for its potential application to problems in coastal engineering is twofold. On one hand, we propose a genetic algorithm hybridized with an extreme learning machine that selects, among the available wave parameters from the nearby buoys, a subset F-nsp with n(sp) parameters that minimizes the H-s reconstruction error. On the other hand, we evaluate to what extent the selected parameters in subset are good enough in assisting other machine learning (ML) regressors (extreme learning machines, support vector machines and gaussian process regression) to reconstruct H-s. The results show that all the ML method explored achieve a good H-s reconstruction in the two different locations studied (Caribbean Sea and West Atlantic).

• 出版日期2015-8