Predicting USCS soil classification from soil property variables using Random Forest

• A Random Forest model for predicting soil USCS classifications was tested.
• USDA textural class by itself is a poor predictor of USCS classification.
• Additional soil property variables are needed to accurately predict USCS class.
• The use of USDA–USCS crosswalk tables should be avoided.

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.