Machine learning (ML) is a collection of methods used to develop understanding and
predictive capability by learning relationships embedded in data. ML methods are becoming …

Given the devastating losses incurred by past major earthquake events together with the
ever-increasing global seismic exposures due to population growth and urbanization …

Conventional ground motion models have extensively been established worldwide based
on classical regression analysis of records. Alternatively, advanced nonparametric machine …

Accurate prediction of soil seismic response is necessary for geotechnical engineering. The
conventional physics-based models such as the finite element method (FEM) usually fail to …

For decades, earthquake prediction has been the focus of research using various methods
and techniques. It is difficult to predict the size and location of the next earthquake after one …

Data-driven ground motion models (GMMs) for the average horizontal component from
shallow crustal continental earthquakes in active tectonic regions are derived using a subset …

This study develops a data‐driven framework to improve the prediction of site amplification
in ground‐motion models (GMM) using horizontal‐to‐vertical spectral ratios (HVSR) proxies …

Turkey is characterized by a high level of seismic activity attributed to its complex tectonic
structure. The country has a dense network to record earthquake ground motions; however …

This research paper presents an analysis of a dataset covering significant earthquakes over
the past century, sourced from a publicly accessible seismic database. The dataset includes …

Ground motion models (GMMs) are an essential tool for seismic hazard analysis. They are
used for developing predictive relationships to estimate the expected levels of seismic …