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.

本文对经典线性和智能非线性时间序列方法进行短期最大波高预测进行了比较研究。用于完成地中海沿岸测量用例的应用模型包括普通线性回归（LR），自回归综合移动平均值（ARIMA），人工神经网络（ANN）和遗传规划（GP）。该研究还介绍了一种称为ensemble GP的新的进化集成模型，该模型通过一个进化过程整合了有效模型的预测。独立模型的结果表明，线性模型和非线性模型都为季节性规模的短期最大波高后兆预报提供了相同的精度。所提出的集成模型可以显着提高独立模型的预测准确性。新模型可以预测最大波高，其均方根误差小于5 cm，Nash-Sutcliff效率大于0.97。它是显式的并且可以确保简约条件，因此建议在实践中使用。