Hybrid Machine Learning Models for Soil Saturated Conductivity Prediction

The hydraulic conductivity of saturated soil is a crucial parameter in the study of any engineering problem concerning groundwater. Hydraulic conductivity mainly depends on particle size distribution, soil compaction, and properties that influence aggregation and water retention. Generally, finding simple and accurate analytical equations between the hydraulic conductivity of soil and the characteristics on which it depends is a very hard task. Machine learning algorithms can provide excellent tools for tackling highly nonlinear regression problems. Additionally, hybrid models resulting from the combination of multiple machine learning algorithms can further improve the accuracy of predictions. Five different models were built to predict saturated hydraulic conductivity using a dataset extracted from the Soil Water Infiltration Global database. The models were based on different predictors. Seven variants of each model were compared, replacing the implemented algorithm. Three variants were based on individual models, while four variants were based on hybrid models. The employed individual machine learning algorithms were Multilayer Perceptron, Random Forest, and Support Vector Regression. The model based on the largest number of predictors led to the most accurate predictions. In addition, across all models, hybrid variants based on all three algorithms and hybridized variants of Random Forest and Support Vector Regression proved to be the most accurate (R-2 values up to 0.829). However, all variants showed a tendency to overestimate conductivity in soils where it is very low.

Automated summary

The hydraulic conductivity of saturated soil, influenced by particle size distribution, soil compaction, and aggregation and water retention properties, plays a crucial role in engineering problems related to groundwater. Machine learning algorithms can provide effective tools for nonlinear regression problems, and hybrid models combining multiple algorithms can further enhance prediction accuracy. Five models were built, based on different predictors, to predict saturated hydraulic conductivity using a dataset from the Soil Water Infiltration Global database. Among all the models, the one with the largest number of predictors showed the most accurate predictions, and hybrid variants combining Random Forest and Support Vector Regression algorithms performed the best.