Earthquake-induced liquefaction severity index prediction using machine learning techniques

Soil liquefaction, a seismic-induced phenomenon, is of significant concern in geotechnical engineering due to its potential to cause severe structural damage and ground instability during earthquakes. This study explores the prediction of the Liquefaction Severity Index (LSI) by integrating extensive borehole investigation data with seismic records from the Kahramanmaraş (M_w 7.8) and Hatay (M_w 6.4) earthquakes that occurred in 2023. Nine machine learning models, Random Forest (RF), M5P, REPTree, IBk, Random Tree (RT), Gaussian Processes (GP), SMOreg, Locally Weighted Learning (LWL), and Linear Regression (LR), were employed with 10-fold cross-validation to ensure reliable predictions. Twelve geotechnical and seismic parameters, groundwater level, earthquake magnitude, peak ground acceleration, V_s30, dominant frequency, dominant period, longitudinal wave velocity, dynamic modulus of elasticity, dynamic shear modulus, modulus of incompressibility, standard penetration test (SPT) values, and cyclic stress ratio (CSR) values, were utilized as inputs. The analysis results were evaluated with respect to RMSE, MAE, R², RAE, P/M, error category limits, Taylor diagram, and relative importance of input parameters. Among the models, Random Forest outperformed with an R² of 0.94, MAE of 2.35, with minimal prediction errors, followed by M5P and REPTree. Error analysis indicated that 80% of Random Forest and REPTree predictions fell within ±7, while M5P showed slightly higher variability. Model-based feature ranking demonstrated that Cyclic Stress Ratio (CSR), Ground Water Level (GWL), and Standard Penetration Test (SPT) value emerged as dominant predictors. These findings highlight the study's contribution to developing a reliable, data-driven framework for LSI prediction, offering a robust basis for improving site-specific liquefaction risk assessment and informed geotechnical decision-making in future seismic events.