Maximum wave height hindcasting using ensemble linear-nonlinear models

Abstract

A comparative study between classic linear and intelligent nonlinear time series approaches for short-term maximum wave height forecasting is presented in this study. The applied models to accomplish a use case for onshore measurements from the Mediterranean Sea include ordinary linear regression (LR), autoregressive integrated moving average (ARIMA), artificial neural networks (ANN), and genetic programming (GP). The study also introduces a new evolutionary ensemble model called ensemble GP, which integrates effective models’ forecasts through an evolutionary procedure. The results from standalone models showed that both linear and nonlinear models provide the same accuracy for short-term maximum wave height hindcasting on a seasonal scale. The proposed ensemble model can enhance the forecasting accuracy of standalone models markedly. The new model can forecast maximum wave heights with the root mean squared errors less than 5 cm and Nash-Sutcliff efficiency more than 0.97. It is explicit and secures parsimony conditions, thus it is proposed to be used in practice.