Enhancing Soil Liquefaction Prediction: Overcoming Data Challenges in SPT-Based Machine Learning with Imputation Technique

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Machine learning identifies soil liquefaction risks in geotechnical engineering, suggesting that imputation methods enhance predictive accuracy.

The Random Forest model achieved an overall accuracy of 90.71%, indicating robust performance in predicting soil liquefaction.
Imputation techniques significantly improved the data-driven predictive model for assessing seismic impact on soil properties.
Assessment involved feature selection and data preprocessing, including normalization and outlier treatment for better learning outcomes.
These findings highlight the utility of advanced machine learning in addressing challenges in geotechnical earthquake engineering.

Fadliansyah et al. (2025) studied this question.

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Machine learning identifies soil liquefaction risks in geotechnical engineering, suggesting that imputation methods enhance predictive accuracy.