Sediment Identification Using Machine Learning Classifiers in a Mixed-Texture Dredge Pit of Louisiana Shelf for Coastal Restoration

Abstract

Machine learning classifiers have been rarely used for the identification of seafloor sediment types in the rapidly changing dredge pits for coastal restoration. Our study uses multiple machine learning classifiers to identify the sediment types of the Caminada dredge pit in the eastern part of the submarine sandy Ship Shoal of the Louisiana inner shelf of the United States (USA), and compares the performance of multiple supervised classification methods. High-resolution bathymetry and backscatter data, as well as 58 sediment grab samples were collected in the Caminada pit in August 2018, about two years after dredging. Two primary features (bathymetry and backscatter) and four secondary features were selected in the machine learning models. Three supervised classifications were tested in the study area: Decision Trees, Random Forest, and Regularized Logistic Regression. The models were trained using three different combinations of features: (1) all six features, (2) only bathymetry and backscatter features, and (3) a subset of selected features. The best performing model was the Random Forest method, but its performance was relatively poor when dealing with a few mixed (sand and mud) surficial sediment samples. The model provides a new and efficient method to predict the change of sediment distribution inside the Caminada pit over time, and is more reliable when predicting mixed bed with rough pit bottoms. Our results can be used to better understand the impacts on biological communities by (1) direct defaunation after initial sand excavation, (2) later mud accumulation in topographic lows, and (3) other geological and physical processes. In the future, the deposition and redistribution of mud inside the Caminada pit will continue, likely impacting benthos and water quality. Backscatter, roughness derived from bathymetry, rugosity derived from backscatter, and bathymetry (in the importance order from high to low) were identified as the most effective predictors of sediment texture for mineral resources management.