Prediction of soil-water retention curves in unsaturated soils based on stacked generalization

The soil-water retention curve (SWRC) plays a pivotal role in understanding water movement across numerous geological engineering applications. Despite significant advancements in theoretical modeling approaches, accurate prediction of SWRCs remains challenging due to the inherently sparse and incomplete nature of site-specific data. This study compiled a comprehensive dataset of SWRCs spanning a wide suction range from various published literature sources. Based on this dataset, multiple machine learning (ML) algorithms were employed to predict SWRCs. The performance of each algorithm was evaluated and ranked using four statistical indicators that quantify simulation accuracy. Feature importance analysis was subsequently conducted to reduce dimensionality by eliminating weakly correlated variables, thereby enhancing both model adaptability and computational efficiency. Following dimensionality reduction, a base learner pool was constructed and integrated through stacked generalization to create a multi-algorithm ensemble model. The proposed stacked model demonstrated robust performance in simulating SWRCs across diverse soil types, using only basic physical properties as inputs, achieving accuracy comparable to or marginally superior to the LightGBM model. The principal advantage of the stacked approach lies in its substantially improved accuracy within high suction ranges, effectively overcoming the limitations observed in LightGBM and enhancing the estimation under these conditions. This study provides valuable insights for researchers evaluating SWRCs through ML algorithms and demonstrates the potential of ensemble techniques in geotechnical prediction tasks.