Spatial Data Mining: Predicting Mud Content across the Southwest Australian Margin  

Authors / CoAuthors

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

Abstract

Spatially continuous data of environmental variables is often required for marine conservation and management. However, information for environmental variables is usually collected by point sampling, particularly for the deep ocean. Thus, methods generating such spatially continuous data by using point samples to estimate values for unknown locations become essential tools. Such methods are, however, often data- or even variable- specific and it is difficult to select an appropriate method for any given dataset. In this study, 14 methods (37 sub-methods) are compared using samples of mud content with five levels of sample density across the southwest Australian margin. Bathymetry, distance to coast, and slope were used as secondary variables. Ten-fold cross validation with relative mean absolute error (RMAE) and visual examination were used to assess the performance of these methods. A total of 1,850 prediction datasets were produced and used to assess the performance of the methods. Considering both the accuracy and the visual examination, we found that a combined method, random forest and ordinary kriging (RKrf), is the most robust method. No threshold in sample density was detected in relation to prediction accuracy. No consistent patterns were observed between the performance of the methods and data variation. The RMAE of three most accurate methods is about 30% lower than that of the best methods in previous publications, highlighting the robustness of the methods selected in this study. The limitations of this study were discussed and a number of suggestions were provided for further studies.

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nonGeographicDataset

eCat Id

69340

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Keywords

• External Publication

( Theme )

• marine

Australian and New Zealand Standard Research Classification (ANZSRC)

• Earth Sciences

• Published_Internal

Publication Date

2009-01-01T00:00:00

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Australian Government Security ClassificationSystem    

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Creative Commons Attribution 4.0 International Licence    

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geoscientificInformation

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Spatial Resolution

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