Modeling the Importance of Ground and Strong-Motion Variables on the Damage Status in the 2023 Kahramanmaraş Earthquakes Using Supervised Machine Learning

The dataset incorporates variables such as Vs30, f0, and EBd to evaluate local ground conditions, in addition to strong-motion parameters including PGA, Repi, and Rrup, with the damage status acting as the target parameter. The station-specific parameters for the 44 available stations within the AFAD seismological network were retrieved from the TADAS database (https://tadas.afad.gov.tr/). This database functions as a comprehensive repository, encompassing active and passive seismic data that represent the site conditions of AFAD stations, as well as strong-motion parameters associated with earthquakes in Türkiye.