Estimating soil–water characteristic curve (SWCC) using machine learning and soil micro-porosity analysis

This study explores soil water characteristic curve (SWCC) prediction through informatics and machine learning. Utilizing these techniques, SWCC prediction was significantly simplified, enabled by the Orange.3 data mining software's integration of diverse soil properties. This integration eliminated the need for extensive programming, establishing a link between scientific insights and engineering applications. Limitations emerged in models relying solely on matric suction for SWCC prediction, evident through a Mean Absolute Error exceeding 0.08 and an R-squared value below 40% in the test dataset. To enhance accuracy, a comprehensive approach encompassing various soil properties, such as bulk density, organic carbon content, and micro-porosity characteristics, was employed. The Gradient Boosting algorithm excelled, yielding near-perfect SWCC estimations with RMSE and P_i values of 0.016 and 0.03, respectively. Likewise, AB, Random Forest, and Tree models displayed highly accurate predictions with RMSE and P_i values below 0.03 and 0.04, respectively. However, Neural Network, SVM, kNN, and Linear Regression models showed no improvements, even with added soil properties. Feature importance analysis highlighted matric suction's critical role in select models and soil micro-porosity characteristics' contribution to lowering RMSE by up to 0.04. These findings are pivotal in understanding errors in SWCC prediction, especially in cases of matric suctions surpassing the SWCC inflection point, with these errors, though present, minimally impacting model efficacy due to diminishing variations at high matric suctions.