Consequences of spatial structure in soil–geomorphic data on the results of machine learning models

Daehyun Kima Department of Geography, Institute for Korean Regional Studies, Seoul National University, Seoul, South KoreaCorrespondence[email protected]
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Insang Songb Department of Geography, University of Oregon, Eugene, OR, USAView further author information

Lorrayne Miralhac Department of Food, Agricultural, and Biological Engineering, The Ohio State University, Columbus, OH, USAView further author information

Daniel R. Hirmasd Department of Plant and Soil Science, Texas Tech University, Lubbock, Texas, USAView further author information

Ryan W. McEwane Department of Biology, University of Dayton, Dayton, OH, USAView further author information

Tom G. Muellerf Heartland Geospatial, LLC, Waukee, IA, USAView further author information

Pavel Šamonilg Department of Forest Ecology, The Silva Tarouca Research Institute, Brno, Czech Republic;h Department of Forest Botany, Dendrology and Geobiocoenology, Mendel University in Brno, Brno, Czech RepublicView further author information

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Consequences of spatial structure in soil–geomorphic data on the results of machine learning models

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Consequences of spatial structure in soil–geomorphic data on the results of machine learning models

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