A Local Weighted Linear Regression (LWLR) Ensemble of Surrogate Models Based on Stacking Strategy: Application to Hydrodynamic Response Prediction for Submerged Floating Tunnel (SFT)

Abstract

Ensemble of metamodels/surrogate models (EM), built based on individual ones, is favoured as an approximation for expensive physical and high-fidelity numerical experiments where individual models with different assumptions on the underlying response functions, datasets, and model structures can be fused more robustly. In this study, a local weighted linear regression (LWLR) approach for constructing the EM, namely the local weighted linear regression ensemble of metamodels (LWLR-EM), is proposed based on the stacking strategy, aiming to address two significant issues in the construction process of EM: 1) it is often unfeasible to obtain sufficient additional points to enhance the performance of EM through high-fidelity numerical simulations and/or physical experiments; 2) underfitting occurs in many cases. To well address these two issues, k-fold cross-validation and local weighted strategies are correspondingly adopted in the metamodel ensembling. Through extensive verification and comparison with existent ensemble methods on typical benchmark test functions, the LWLR-EM is found to perform favourably with competitive accuracy, enhanced robustness, and improved generalization in the prediction task. Thereafter, an engineering practice of submerged floating tunnel (SFT) with five input parameters is considered to further examine the performance of surrogate models. The results show that the proposed LWLR-EM is featured with desirable prediction power and can serve as a competitive alternative in applied ocean engineering.