Advances in spatial modelling: hybrids of machine learning methods and the existing spatial interpolation methods and their application to marine environmental variables  

Authors / CoAuthors

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

Abstract

Spatial interpolation methods for generating spatially continuous data from point samples of environmental variables are essential for environmental management and conservation. They may fall into three groups: non-geostatistical methods (e.g., inverse distance weighting), geostatistical methods (e.g., ordinary kriging) and combined/hybrid methods (e.g. regression kriging); and their performance is often data-specific (Li and Heap, 2008). Because of the robustness of machine learning methods, like random forest and support vector machine, in data mining fields, we introduced them into spatial statistics by applying them to the spatial predictions of seabed mud content in combination with existing spatial interpolation methods (Li et al., 2011). This development can be viewed as an extension of the combined methods from statistical methods to machine learning field. These applications have significantly improved the prediction accuracy and opened an alternative source of methods for spatial interpolation. Given that they have only been applied to one variable, several questions remain, namely: are they dataset- specific? How reliable are their predictions for different datasets and variables? Could other machine learning methods (such as boosted regression trees) improve the spatial interpolations? To address these questions, we experimentally compared the predictions of several methods for sand content on the southwest Australian marine margin. We tested a variety of existing spatial interpolation methods, machine learning methods and their combinations. In this study, we discuss the experimental results and the value of this advancement in spatial interpolation, visually examine the spatial predictions, and compare the results with the findings in the previous publications. The outcomes of this study can be applied to the spatial prediction of marine and terrestrial environmental variables.

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nonGeographicDataset

eCat Id

73769

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Keywords

• External Publication

( Theme )

• environmental

( Theme )

• geoscience

( Theme )

• numerical modelling

( Theme )

• marine

Australian and New Zealand Standard Research Classification (ANZSRC)

• Earth Sciences

• Published_Internal

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2012-03-08T00:00:00

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Creative Commons Attribution 4.0 International Licence    

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