Can machine learning methods be applied for spatial predictions of environmental properties?  

Authors / CoAuthors

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

Abstract

Spatial interpolation methods for generating spatially continuous data from point locations of environmental variables are essential for ecosystem management and biodiversity conservation. They can be classified into three groups (Li and Heap 2008): 1) non-geostatistical methods (e.g., inverse distance weighting), 2) geostatistical methods (e.g., ordinary kriging: OK) and 3) combined methods (e.g. regression kriging). Machine learning methods, like random forest (RF) and support vector machine (SVM), have shown their robustness in data mining fields. However, they have not been applied to the spatial prediction of environmental variables (Li and Heap 2008). Given that none of the existing spatial interpolation methods is superior to the others, several questions remain, namely: 1) could machine learning methods be applied to the spatial prediction of environmental variables; 2) how reliable are their predictions; 3) could the combination of these methods with the existing interpolation methods improve the predictions; and 4) what contributes to their accuracy? To address these questions, we conducted a simulation experiment to compare the predictions of several methods for mud content on the southwest Australian marine margin. In this study, we discuss results derived from this experiment, visually examine the spatial predictions, and compare the results with the findings in previous publications. The outcomes of this study have both practical and theoretical importance and can be applied to the spatial prediction of a range of environmental variables for informed decision making in environmental management. This study reveals a new direction in and provides alternative methods for spatial interpolation in environmental sciences.

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nonGeographicDataset

eCat Id

70465

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Keywords

• External Publication

( Theme )

• environmental

( Theme )

• GIS

( Theme )

• numerical modelling

( Theme )

• geoscience

( Theme )

• marine

Australian and New Zealand Standard Research Classification (ANZSRC)

• Earth Sciences

• Published_Internal

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2010-01-01T00:00:00

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