Chapter 11

Predicting Seabed Hardness Using Random Forest in R

Jin Li, P. Justy, W. Siwabessy, Maggie Tran, Zhi Huang and Andrew D. Heap,    Geoscience Australia, GPO Box 378, Canberra, ACT 2601, Canberra, Australia

Abstract

The spatial information of the seabed biodiversity is important for marine zone management in Australia. The biodiversity is often predicted using spatially continuous data of seabed biophysical properties. Seabed hardness is an important property for predicting the biodiversity and is often inferred from multibeam backscatter data. Seabed hardness can also be inferred based on underwater video footage that is, however, only available at a limited number of sampled locations. In this study, we predict the spatial distribution ...

Get Data Mining Applications with R now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.