Ground‐motion prediction models (GMPMs) have been developed to estimate seismic intensity considering earthquake magnitude, source‐to‐site distance, site condition, and so on. This study proposes GMPMs to predict 5% damped pseudospectral acceleration (PSA) for 27 periods ranging from 0.01 to 10 s in Korea, based on three machine‐learning techniques (i.e., artificial neural network [ANN], random forest [RF], and gradient boosting [GB]). We use 1189 ground motions recorded at 50 surface stations during the 77 earthquakes with a local magnitude (ML) greater than 3.0, including the Gyeongju and Pohang earthquakes with ML of 5.8 and 5.4, respectively. We compare the performances of the three machine‐learning‐based models and the classical regression‐based model in terms of the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), standard deviation of residuals, and between‐event and within‐event residuals. The GB‐based model shows the best performance. In addition, we analyze the working process of the GB‐based model using variable importance and partial dependence (PD) plots. Among the five independent variables (ML, epicentral distance [Repi], average shear‐wave velocity of the upper 30 m [VS30], focal depth, and slope angle) used in this study, ML and Repi are the most influential variables and show strong correlations with PSAs. We apply the GB‐based model to three recent earthquakes larger than ML 3.0, and the model accurately predicts the PSAs at various stations. We also generate maps of estimated PSA (PSAest) values for the four periods (T = 0.01, 0.1, 1, and 3 s) for the scenario earthquake with an ML of 5.0. We provide a method for training the GB‐based model using the Python library, which can enhance the ground‐motion prediction not only in Korea but also worldwide, and an executable version of the validated GB‐based model.

You do not have access to this content, please speak to your institutional administrator if you feel you should have access.