Novel hybrid spatial predictive methods of machine learning and geostatistics with applications to terrestrial and marine environments in Australia  

Authors / CoAuthors

Li, J. | Sanabria, L.A.

Abstract

The accuracy of spatially continuous environmental data, usually generated from point samples using spatial prediction methods (SPMs), is crucial for evidence-informed environmental management and conservation. Improving the accuracy by identifying the most accurate methods is essential, but also challenging since the accuracy is often data specific and affected by multiple factors. Because of the high predictive accuracy of machine learning methods, especially random forest (RF), they were introduced into spatial statistics by combining them with existing SPMs, which resulted in new hybrid methods with improved accuracy. This development opened an alternative source of methods for spatial prediction. In this study, we introduced these hybrid methods, along with the modelling procedure adopted to develop the final predictive models. These methods were compared with the commonly used SPMs in R using cross-validation techniques based on both marine and terrestrial environmental data. We also addressed the following questions: 1) whether they are data-specific for marine environmental data, 2) whether input predictors affect their performance, and 3) whether they are equally applicable to terrestrial environmental data? This study provides suggestions and guidelines for the application of these hybrid methods to spatial predictive modelling not only in environmental sciences, but also in other relevant disciplines.

Product Type

nonGeographicDataset

eCat Id

83106

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Keywords

• External Publication

( Theme )

• marine

( Theme )

• NERP

Australian and New Zealand Standard Research Classification (ANZSRC)

• Earth Sciences

• Published_Internal

Publication Date

2015-01-01T00:00:00

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Australian Government Security ClassificationSystem    

Classification - unclassified

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Creative Commons Attribution 4.0 International Licence    

Access - restricted

Use - license

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Purpose

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unknown

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geoscientificInformation

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Unknown

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Spatial Resolution

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