Evaluation of liquefaction potential in central Taiwan using random forest method

Abstract

Abstract Liquefaction is a significant geotechnical hazard in seismically active regions like Taiwan, threatening infrastructure and public safety. Accurate prediction models are essential for assessing soil susceptibility to liquefaction during seismic events. This study evaluates liquefaction potential in central Taiwan using the random forest (RF) method. The RF models were developed with a dataset of 540 soil and seismic parameter sets, including depth, effective and total overburden stresses, SPT-N values, fine soil content, earthquake magnitude, peak ground acceleration, and historical liquefaction occurrences. Rigorous validation techniques, such as cross-validation and comparisons with observed liquefaction events, confirm the RF model’s effectiveness, achieving an accuracy of 98.89%. The model also quantifies predictor importance, revealing that the SPT-N value is the most critical soil factor, while peak ground acceleration is the key seismic factor for liquefaction prediction. Notably, the RF model outperforms simplified procedures in accuracy, even with fewer input factors. Our case studies show that an accuracy of over 95% can still be achieved, highlighting the RF model’s superior performance compared to conventional methods, which struggle to reach similar levels.

Keywords

• Liquefaction
• Seismic activity
• Artificial neural network
• Random forest
• Earthquake
• Potential