Prediction of seismic amplification factors in India using machine learning models

Predicting ground motion amplification factors (AFs) is essential for constructing resilient infrastructure and managing earthquake risks in countries like India, which has a diverse range of geological and geophysical characteristics. The focus of this study was on predicting AFs across different soil sites and recorded ground motions using various machine learning (ML) methods. The training and testing datasets for AFs were created numerically using DeepSoil. A one-dimensional ground response analysis using equivalent linear methods was conducted at 124 soil sites across 20 regions of India. For this analysis, spectrum-compatible time histories were used. To predict AFs using ML models, it is vital to incorporate diverse parameters that represent both soil and seismic ground motion traits. Key indicators include the soil depth, shear wave velocity and fundamental period, while parameters such as magnitude, epicentral distance and peak ground acceleration illustrate ground motion features. In this research, an artificial neural network (ANN), random forest regression (RFR) and extreme gradient boosting (XGBoost) were trained and assessed for their predictive efficacy. It was found that the soil site's primary time period and shear wave velocity are crucial for predicting AFs. The ML models were found to excel at estimating AFs in Indian regions, with the XGBoost model outperforming the ANN and RFR models.