Predicting Seabed Mud Content across the Australian Margin: Comparison of Statistical and Mathematical Techniques Using a Simulation Experiment  

Authors / CoAuthors

Li, J. | Potter, A. | Huang, Z. | Daniell, J.J. | Heap, A.D.

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.

Product Type

document

eCat Id

70150

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Keywords

• GA PublicationRecord

( Theme )

• GIS

( Theme )

• numerical modelling

( Theme )

• environmental

( Theme )

• geoscience

( Theme )

• model

( Theme )

• marine

• AU-EEZ

Australian and New Zealand Standard Research Classification (ANZSRC)

• Earth Sciences

• Published_External

Publication Date

2010-01-01T00:00:00

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Australian Government Security ClassificationSystem    

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Creative Commons Attribution 4.0 International Licence    

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Use - license

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Purpose

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unknown

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geoscientificInformation

Series Information

Record 2010/011

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Unknown

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Extents

[-50.0, -8.0, 105.0, 165.0]

Reference System

Spatial Resolution

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