An improved stacking-based model for wave height prediction

Abstract

Wave height prediction is hampered by the volatility and unpredictability of ocean data. Traditional single predictors are inadequate in capturing this complexity, and weighted fusion methods fail to consider inter-model correlations, resulting in suboptimal performance. To overcome these challenges, we presented an improved stacking-based model that combined the long short-term memory (LSTM) network with extremely randomized trees (ET) for wave height prediction. Initially, features with weak correlation to wave height were excluded using the Pearson correlation coefficient. Subsequently, a stacking ensemble tailored for time series cross-validation was deployed, employing LSTM and ET as base learners to capture temporal and feature-specific patterns, respectively. Lasso regression was utilized as the meta-learner, harmonizing these insights to improve accuracy by leveraging the strengths of each model across different dimensions of the data. Validation using datasets from four buoy stations demonstrated the superior predictive capability of our proposed model over single predictors such as temporal convolutional networks (TCN) and XGBoost, and fusion methods like LSTM-ET-BP.

Keywords

• wave height prediction
• stacking ensemble
• time series split
• lstm network
• extremely randomized trees