Assessing the effect of various training and testing set ratios to model the satellite derived bathymetry

Abstract

Abstract Depth determination in shallow water area is critical to model for instance, a detailed shoreline position, a change in beach topography, and the potency of beach erosion. Multispectral images can provide a complete map of areas that are difficult to map by conventional hydrographic surveys due to their logistics difficulties and limited spatial coverage. The aim of this study is to evaluate the effect of various training and testing set ratios to model bathymetric data from remotely sensed imagery. This research applied three methods to derive shallow water bathymetric data tested on two subsets located along Tanjung Kelayang coastal areas. The methods combined echo-sounding measurements and the reflectance of blue, green, red and near infrared of Sentinel 2A image with 10 m spatial resolution. In the experiment, the echo-sounding data set was split into training and testing set in three different ratios to see the effect of these various training and testing ratios to the accuracy of all algorithms. From the results, we can see that all models perform well in estimating bathymetric data for the shallow water depth, however, the accuracies were slightly changing by the variation of the training and testing data included in the model. In general, all methods provide a comparable performance for shallow water depth with RMSE less than 1 m and can be used effectively for deriving accurate and updated medium resolution bathymetric maps.