![]() ![]() Our objective was to compare multiple machine learning models and covariate sets for predicting soil taxonomic classes at three geographically distinct areas in the semi-arid western United States of America (southern New Mexico, southwestern Utah, and northeastern Wyoming). However, there is little guidance as to which, if any, machine learning model and covariate set might be optimal for predicting soil classes across different landscapes. ![]() Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. Machine learning is a general term for a broad set of statistical modeling techniques. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. ![]() Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |