Consequences of spatial structure in soil–geomorphic data on the results of machine learning models-Reference-Cited by-同舟云学术

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

Author:

Kim Daehyun¹,Song Insang²,Miralha Lorrayne³,Hirmas Daniel R.⁴,McEwan Ryan W.⁵,Mueller Tom G.⁶,Šamonil Pavel⁷⁸

Affiliation:

1. Department of Geography, Institute for Korean Regional Studies, Seoul National University, Seoul, South Korea

2. Department of Geography, University of Oregon, Eugene, OR, USA

3. Department of Food, Agricultural, and Biological Engineering, The Ohio State University, Columbus, OH, USA

4. Department of Plant and Soil Science, Texas Tech University, Lubbock, Texas, USA

5. Department of Biology, University of Dayton, Dayton, OH, USA

6. Heartland Geospatial, LLC, Waukee, IA, USA

7. Department of Forest Ecology, The Silva Tarouca Research Institute, Brno, Czech Republic

8. Department of Forest Botany, Dendrology and Geobiocoenology, Mendel University in Brno, Brno, Czech Republic

Funder

National Science Foundation

National Research Foundation of Korea

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

Reference73 articles.

1. Abu-Mostafa YS Magdon-Ismail M Lin HT. 2012. Learning from data. United States: AMLBook.

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