_:n5e6e90b5bba04fda9428c60473b84661xb9
<http://schema.org/description>
"Machine learning algorithms find frequent application in spatial prediction\nof biotic and abiotic environmental variables. However, the characteristics of\nspatial data, especially spatial autocorrelation, are widely ignored. We\nhypothesize that this is problematic and results in models that can reproduce\ntraining data but are unable to make spatial predictions beyond the locations\nof the training samples. We assume that not only spatial validation strategies\nbut also spatial variable selection is essential for reliable spatial\npredictions. We introduce two case studies that use remote sensing to predict\nland cover and the leaf area index for the \"Marburg Open Forest\", an open\nresearch and education site of Marburg University, Germany. We use the machine\nlearning algorithm Random Forests to train models using non-spatial and spatial\ncross-validation strategies to understand how spatial variable selection\naffects the predictions. Our findings confirm that spatial cross-validation is\nessential in preventing overoptimistic model performance. We further show that\nhighly autocorrelated predictors (such as geolocation variables, e.g. latitude,\nlongitude) can lead to considerable overfitting and result in models that can\nreproduce the training data but fail in making spatial predictions. The problem\nbecomes apparent in the visual assessment of the spatial predictions that show\nclear artefacts that can be traced back to a misinterpretation of the spatially\nautocorrelated predictors by the algorithm. Spatial variable selection could\nautomatically detect and remove such variables that lead to overfitting,\nresulting in reliable spatial prediction patterns and improved statistical\nspatial model performance. We conclude that in addition to spatial validation,\na spatial variable selection must be considered in spatial predictions of\necological data to produce reliable predictions."
<https://deepai.org/profile/hanna-meyer>