@article{M7CF63615,
title = "Evaluating the Efficiency of Models for Predicting Seismic Building Damage",
journal = "The Transactions of the Korea Information Processing Society",
year = "2024",
issn = "null",
doi = "https://doi.org/10.3745/TKIPS.2024.13.5.217",
author = "Chae Song Hwa, Yujin Lim",
keywords = "Earthquake, Earthquake Damage Prediction, Machine Learning(ml)",
abstract = "Predicting earthquake occurrences accurately is challenging, and preparing all buildings with seismic design for such random events 
is a difficult task. Analyzing building features to predict potential damage and reinforcing vulnerabilities based on this analysis can minimize 
damages even in buildings without seismic design. Therefore, research analyzing the efficiency of building damage prediction models 
is essential. In this paper, we compare the accuracy of earthquake damage prediction models using machine learning classification 
algorithms, including Random Forest, Extreme Gradient Boosting, LightGBM, and CatBoost, utilizing data from buildings damaged during 
the 2015 Nepal earthquake."
}