Seabed modelling with a least-squares support-vector machine and sample cross-validation

Abstract

This paper reports on a study to model seabed surfaces using the least-squares support-vector machine algorithm with a sample cross-validation (CV) method. It starts with a brief overview of the sample selection method of the algorithm and gives two important characteristics of the algorithm. It then focuses on the theory of sample CV and the steps of sample selection using this theory. Finally, to verify the validity of the sample CV method in sample selection for the algorithm, the measured multi-beam bathymetric data are selected to calculate and analyse. It is concluded that the sample CV method can reasonably screen out the sounding training samples with a large contribution to the function model, making the constructed function model more reasonable.