9634d559-7f11-4dea-93c8-fe96ea971062 urn:uuid eng UTF-8 nonGeographicDataset nonGeographicDataset 2025-10-16T06:33:16 creation ISO 19115 2016 Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods 9634d559-7f11-4dea-93c8-fe96ea971062 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. Earth Sciences GA Publication Published_External Record environmental geoscience marine numerical modelling seabed Download the Record (pdf) documentDigital Download the Record (pdf) PDF Unnamed resource documentDigital HTML