Classification of Coastal Benthic Substrates Using Supervised and Unsupervised Machine Learning Models on North Shore of the St. Lawrence Maritime Estuary (Canada)

Abstract

Classification of benthic substrates is a core necessity in many scientific fields like biology, ecology, or geology, with applications branching out to a variety of industries, from fisheries to oil and gas. In the first part, a comparative analysis of supervised learning algorithms has been conducted using geomorphometric features to generate benthic substrate maps of the coastal regions of the North Shore of Quebec in order to establish a quantitative assessment of performance to serve as a benchmark. In the second part, a new method using Gaussian mixture models is showcased on the same dataset. Finally, a side-by-side comparison of both methods is featured to provide a qualitative assessment of the new algorithm’s ability to match human intuition.

Keywords

• benthic habitat mapping
• benthic habitat classification
• supervised machine learning
• unsupervised machine learning
• artificial intelligence