Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction

Hanna Meyer, Christoph Reudenbach, Stephan Wöllauer and Thomas Nauss

Ecological Modelling, 2019, vol. 411, issue C

Abstract: Machine learning algorithms find frequent application in spatial prediction of biotic and abiotic environmental variables. However, the characteristics of spatial data, especially spatial autocorrelation, are widely ignored. We hypothesize that this is problematic and results in models that can reproduce training data but are unable to make spatial predictions beyond the locations of the training samples. We assume that not only spatial validation strategies but also spatial variable selection is essential for reliable spatial predictions.

Keywords: Cross-validation; Environmental monitoring; Machine learning; Overfitting; Random Forests; Remote sensing (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:411:y:2019:i:c:s0304380019303230

DOI: 10.1016/j.ecolmodel.2019.108815

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