지진으로 인한 건물 손상 예측 모델의 효율성 분석
Evaluating the Efficiency of Models for Predicting Seismic Building Damage
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초록
지진 발생은 정확히 예측하기 어렵고, 이러한 무작위성을 갖는 사건에 대비하여 모든 건물에 내진 설계를 도입하는 것은 현실적으로 어려운과제이다. 건물의 특징 분석을 통한 건물 손상 예측을 기반으로 건물의 취약점을 보완한다면, 내진 설계를 도입하지 않은 건물에서도 피해를 최소화할수 있으므로 건물 손상 예측 모델의 효율성을 분석하는 연구가 필요하다. 본 논문에서는 2015년 네팔 대지진으로 인해 손상된 건물 데이터를활용하여 Random Forest, Extreme Gradient Boosting, LightGBM, CatBoost 기계학습 분류 알고리즘을 사용하여 지진 피해 예측 모델의 정확도를비교하였다.
Abstract
Predicting earthquake occurrences accurately is challenging, and preparing all buildings with seismic design for such random eventsis a difficult task. Analyzing building features to predict potential damage and reinforcing vulnerabilities based on this analysis can minimizedamages even in buildings without seismic design. Therefore, research analyzing the efficiency of building damage prediction modelsis essential. In this paper, we compare the accuracy of earthquake damage prediction models using machine learning classificationalgorithms, including Random Forest, Extreme Gradient Boosting, LightGBM, and CatBoost, utilizing data from buildings damaged duringthe 2015 Nepal earthquake.
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