Evaluating the Efficiency of Models for Predicting Seismic Building Damage 


Vol. 13,  No. 5, pp. 217-220, May  2024
https://doi.org/10.3745/TKIPS.2024.13.5.217


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  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.

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[IEEE Style]

C. S. Hwa and Y. Lim, "Evaluating the Efficiency of Models for Predicting Seismic Building Damage," The Transactions of the Korea Information Processing Society, vol. 13, no. 5, pp. 217-220, 2024. DOI: https://doi.org/10.3745/TKIPS.2024.13.5.217.

[ACM Style]

Chae Song Hwa and Yujin Lim. 2024. Evaluating the Efficiency of Models for Predicting Seismic Building Damage. The Transactions of the Korea Information Processing Society, 13, 5, (2024), 217-220. DOI: https://doi.org/10.3745/TKIPS.2024.13.5.217.

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