An Advanced Soil Classification Method Employing the Random Forest Technique in Machine Learning

Abstract

Soil classification is essential for understanding soil properties and their suitability for conveying the characteristics of soil types. In this study, we present a prediction of soil classification using fewer soil variables by employing the random forest (RF) technique in machine learning. This study compiled the parameters outlined in the unified soil classification system (USCS), a widely used method for categorizing soils based on their properties and behavior. These parameters, encompassing grain size distribution, Atterberg limits, the coefficient of uniformity, and the coefficient of curvature, were defined within specific ranges to create a synthetic database for training the RF model. The importance of input factors in soil classification was assessed using the out-of-bag samples in RF. Through rigorous validation techniques, including cross-validation, the performance of the RF model is thoroughly assessed, demonstrating its capability to accurately evaluate soil classification. The findings indicate that the RF model presented in this study exhibits a promising alternative, providing automated and accurate classification based on soil data. Notably, the model indicates that the coefficients of uniformity and gradation are insignificant for soil classification and can predict soil types even when these factors are missing, a feat that traditional methods struggle to achieve.

Keywords

• soil
• unified soil classification system
• random forest
• grain size
• Atterberg limits