用集成学习提升地震后建筑损毁等级预测精度

In the aftermath of major earthquakes, evaluating structural and infrastructural damage is vital for coordinating post-disaster response efforts. This includes assessing damage's extent and spatial distribution to prioritize rescue operations and resource allocation. Accurately estimating damage grades to buildings post-earthquake is paramount for effective response and recovery, given the significant impact on lives and properties, underscoring the urgency of streamlining relief fund allocation processes. Previous studies have shown the effectiveness of multi-class classification, especially XGBoost, along with other machine learning models and ensembling methods, incorporating regularization to address class imbalance. One consequence of class imbalance is that it may give rise to skewed models that undervalue minority classes and give preference to the majority class. This research deals with the problem of class imbalance with the help of the synthetic minority oversampling technique (SMOTE). We delve into multiple multi-class classification machine learning, deep learning models, and ensembling methods to forecast structural damage grades. The study elucidates performance determinants through comprehensive feature manipulation experiments and diverse training approaches. It identifies key factors contributing to seismic vulnerability while evaluating model performance using techniques like the confusion matrix further to enhance understanding of the effectiveness of earthquake damage prediction.