### Method for Assessing Earthquake Fatalities Based on Machine Learning  
Considering the characteristics of small sample sizes, nonlinearity, and high dimensionality in seismic disaster data acquisition, in recent years, numerous scholars have applied intelligent analysis methods to earthquake casualty prediction models, such as back propagation neural networks (BPNNs), support vector machines (SVMs), and artificial neural networks (ANNs). These methods are not constrained by the numerous assumptions of traditional multiple linear regression (MLR). By simulating human learning processes, they extract hidden, effective, and interpretable patterns of earthquake casualties from large volumes of seismic disaster data. [PERSON] et al. [24], using earthquake disaster data from Iran, employed the BPNN method to develop an assessment model for the extent and distribution of casualties caused by building damage, which demonstrated higher accuracy than the Coburn and Spence model for the 2003 Bam earthquake in Iran. [PERSON] et al. [25], based on data from 21 earthquakes in Turkey with Mw > 5, used earthquake occurrence time, magnitude, and population density as predictors to establish an ANN-based earthquake casualty prediction model. [PERSON] et al. [26] developed a model for predicting earthquake fatalities using a BP neural network optimized by genetic algorithms (GA). [PERSON] et al. [27], based on deep learning algorithms, used parameters such as magnitude, epicentral intensity, earthquake occurrence time, focal depth, and population density as inputs, while testing, training, and validating 295 earthquake cases in mainland China, to build a rapid post-earthquake casualty assessment model. The model was further optimized by incorporating building damage impact coefficients and regional correction coefficients for secondary disasters. While the aforementioned algorithms have achieved certain results, traditional methods such as SVM and BP neural networks have limitations in the context of earthquake fatality prediction, a typical problem characterized by small samples, high dimensionality, and strong nonlinearity. SVM has strong theoretical support and generalization ability under small sample conditions but is highly sensitive to the choice of kernel function, struggles to handle complex nonlinear interactions between variables, and requires significant effort in parameter tuning and hyperparameter optimization, posing challenges in practical modeling and deployment. BP neural networks, on the other hand, possess powerful nonlinear modeling and adaptive feature learning capabilities but typically require large sample sizes for training. Otherwise, they are prone to overfitting, and their complex network structures, unstable training processes, and poor interpretability do not provide the transparency and trustworthiness needed for disaster decision-making. In contrast, XGBoost, as an ensemble learning algorithm based on gradient boosting trees, provides an effective solution to the aforementioned challenges. [PERSON] et al. [28] developed a large-scale regional model based on household rainstorm disasters in South Korea, utilizing XGBoost, Random Forest (RF), Decision Tree, and KNN models to predict flood damage costs. Additionally, [PERSON] et al. [29] proposed the FloodDamageCast framework, which, after enhancing data through Generative Adversarial Networks (GANs), integrates multiple machine learning methods to perform real-time estimation of building losses in flood disasters, achieving precise identification and rapid early warning for high-damage spatial areas. It progressively optimizes residuals by integrating multiple decision trees, leveraging first- and second-order derivatives to enhance learning efficiency and accuracy, while incorporating regularization terms to control model complexity, thereby exhibiting excellent resistance to overfitting in small-sample datasets.