Predicting Seabed Mud Content across the Australian Margin: Comparison of Statistical and Mathematical Techniques Using a
Simulation Experiment
Dataset Identification:
Resource Abstract:
In this study, we conducted a simulation experiment to identify robust spatial interpolation methods using samples of seabed
mud content in the Geoscience Australian Marine Samples database. Due to data noise associated with the samples, criteria
are developed and applied for data quality control. Five factors that affect the accuracy of spatial interpolation were considered:
1) regions; 2) statistical methods; 3) sample densities; 4) searching neighbourhoods; and 5) sample stratification. Bathymetry,
distance-to-coast and slope were used as secondary variables. Ten-fold cross-validation was used to assess the prediction
accuracy measured using mean absolute error, root mean square error, relative mean absolute error (RMAE) and relative root
mean square error. The effects of these factors on the prediction accuracy were analysed using generalised linear models.
The prediction accuracy depends on the methods, sample density, sample stratification, search window size, data variation
and the study region. No single method performed always superior in all scenarios. Three sub-methods were more accurate than
the control (inverse distance squared) in the north and northeast regions respectively; and 12 sub-methods in the southwest
region. A combined method, random forest and ordinary kriging (RKrf), is the most robust method based on the accuracy and
the visual examination of prediction maps. This method is novel, with a relative mean absolute error (RMAE) up to 17% less
than that of the control. The RMAE of the best method is 15% lower in two regions and 30% lower in the remaining region than
that of the best methods in the previously published studies, further highlighting the robustness of the methods developed.
The outcomes of this study can be applied to the modelling of a wide range of physical properties for improved marine biodiversity
prediction. The limitations of this study are discussed. A number of suggestions are provided for further studies.
Citation
Title Predicting Seabed Mud Content across the Australian Margin: Comparison of Statistical and Mathematical Techniques Using a
Simulation Experiment
Metadata standard for this record:
ANZLIC Metadata Profile: An Australian/New Zealand Profile of AS/NZS ISO 19115:2005, Geographic information - Metadata
standard version:
1.1
Metadata record identifier:
a05f7892-f002-7506-e044-00144fdd4fa6
Metadata record format is ISO19139-2 XML (MI_Metadata)