Daniel R. Gambill, Wade A. Wall, Andrew J. Fulton, Heidi R. Howard
Journal of Terramechanics, Volume 65, June 2016, Pages 85-92, ISSN 0022-4898, http://dx.doi.org/10.1016/j.jterra.2016.03.006.
http://www.sciencedirect.com/science/article/pii/S0022489816300040
Abstract:
Soil classification systems are widely used for quickly and easily summarizing soil properties and provide a shorthand method of communication between scientists, engineers, and end-users. Two of the most widely used soil classification systems are the United States Department of Agriculture (USDA) textural soil classification system and the Unified Soil Classification System (USCS). Unfortunately, not all soil map units are classified according to the USDA or USCS systems, and previous attempts to provide a crosswalk table have been inconsistent. Random Forest machine learning model was used to create a USCS prediction model using USDA soil property variables. Important variables for predicting USCS code from available soil properties were USDA soil textures, percent organic material, and available water storage. Prediction error rates less than 2% were achieved compared to error rates of approximately 40% using crosswalk methods.
Keywords: USDA; USCS; Random Forest model; Crosswalk table
International Society for Terrain-Vehicle Systems