Mapping surface sediment characteristics in enclosed shallow‐marine environments using spatially balanced designs and the random forest algorithm

Abstract

AbstractMapping the sedimentary character of the seafloor in large water‐filled basins is fundamental for understanding landform dynamics to inform research, management, intervention and conservation actions. Seabed mapping methods have undergone considerable development in the last two decades, including the uptake of machine learning approaches for sediment size prediction and classification. However, predictions of surficial sediment characteristics are often hindered by the availability of ground truthing data, their arrangement in space and the modelling approach chosen. Spatially informed sampling designs provide an opportunity to significantly improve the accuracy and uncertainty of predicted sediment distributions. In this study, we apply a machine learning algorithm to predict sediment distributions across Port Phillip Bay, a large (1930 km2) structurally controlled estuary on the southeast coast of Victoria, Australia. Surface sediment samples (n = 252) were collected using a spatially balanced design, ensuring that sampling effort was spread evenly within the embayment with increased sampling intensity placed in more heterogeneous areas. Surficial textural metrics were modelled using the random forest algorithm with bathymetric and hydrodynamic predictor variables. Models highlighted trends in sediment grain size, sorting and composition consistent with predicted wave‐ and current‐induced sediment mobilisation. Model predictions were accurate (normalised‐root mean squared error [NRMSE]: 0.14–0.16); however, standard error was not homogeneous across the study area. Uncertainty maps highlighted areas where additional sampling effort may be needed, including areas where transitional bathymetry impacted surficial sediment character and areas of anthropogenic modifications to the seabed. This study shows the benefits of undertaking spatially informed sample design, block cross‐validation during model fitting and quantifying spatial uncertainty in predictive maps to accurately quantify the fundamental boundary conditions of sediment size. The results of this study are intended to inform local coastal management, including beach renourishment activities. However, approaches outlined are applicable to any study where the seafloor grain size is a fundamental variable in understanding landscape change.