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Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods

Created 16/10/2025

Updated 16/10/2025

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In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content for the AEEZ using samples extracted on August 2010 from Geoscience Australia's Marine Samples Database. The predictive accuracy changes with methods, input secondary variables, model averaging, search window size and the study region but the choice of mtry. No single method performs best for all the tested scenarios. Of the 18 compared methods, RFIDS and RFOK are the most accurate methods in all three regions. Overall, of the 36 combinations of input secondary variables, methods and regions, RFIDS, 6RFIDS and RFOK were among the most accurate methods in all three regions. Model averaging further improved the prediction accuracy. The most accurate methods reduced the prediction error by up to 7%. RFOKRFIDS, with a search window size of 5, an mtry of 4 and more realistic predictions in comparison with the control, is recommended for predicting sand content across the AEEZ if a single method is required. This study provides suggestions and guidelines for improving the spatial interpolations of marine environmental data.

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Additional Info

Field Value
Title Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods
Language eng
Licence Not Specified
Landing Page https://data.gov.au/data/en/dataset/9634d559-7f11-4dea-93c8-fe96ea971062
Contact Point
Geoscience Australia Data
clientservices@ga.gov.au
Reference Period 20/04/2018
Geospatial Coverage Australia
Data Portal Geoscience Australia

Data Source

This dataset was originally found on Geoscience Australia "Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods". Please visit the source to access the original metadata of the dataset:
https://ecat.ga.gov.au/geonetwork/srv/eng/csw/dataset/predicting-seabed-sand-content-across-the-australian-margin-using-machine-learning-and-geostati1