Using Random Forest Algorithm to Predict the Hydraulic Conductivity of Compacted Soil Liners/Covers
Publication: Geo-Congress 2023
ABSTRACT
Compacted soil is widely used as liners and covers in waste containment systems owing to its low hydraulic conductivity (k < 1 × 10–9 m/s). The hydraulic conductivity is often measured in laboratories using flexible-wall parameters or in fields using a lysimeter or infiltrometer, which all require weeks or months to reach equilibrium. In this study, a machine-learning-based predictive model was developed to determine the saturated hydraulic conductivity of compacted soil using the random forest (RF) algorithm. A database was created to train and validate the RF model, which contains the hydraulic conductivity and 12 impact factors of 329 soil samples in North America. The 12 impact factors, covering soil physical properties, hydration characteristics, and compaction conditions, were used in the RF model. A multiple linear regression model was also constructed as a comparison, using the same database and impact factors. The RF model validation indicated that 92% of the predicted hydraulic conductivity has a discrepancy with the measured hydraulic conductivity of fewer than 10 times, and 100% fewer than 100 times. RF model has a higher precision in predicting the hydraulic conductivity of compacted soil than the multiple linear regression model.
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Geo-Congress 2023
Pages: 193 - 200
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Published online: Mar 23, 2023
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