集成深度学习模型在地震烈度预测中的应用——以意大利数据为例
Application of an Ensemble Deep Learning Model in Earthquake Intensity Prediction—A Case Study Using Italian Data
摘要: 本研究探讨了一种基于深度学习的集成策略来预测地震烈度的方法。通过采用意大利地震数据集(INSTANCE)的数据，研究结合了卷积神经网络(CNN)、长短期记忆网络(LSTM)和全连接网络(FCNN)，并运用Bagging算法以提高模型的泛化能力和预测精度。实验结果表明，本研究所提出的集成模型能够有效地预测地震烈度，并对不同烈度级别进行了准确区分。
Abstract: This study explores a deep learning-based ensemble strategy for predicting earthquake intensity. Using data from the Italian earthquake dataset (INSTANCE), the research integrates Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Fully Connected Neural Networks (FCNN), while employing the Bagging algorithm to enhance the model’s generalization ability and prediction accuracy. The experimental results demonstrate that the proposed ensemble model effectively predicts earthquake intensity and accurately distinguishes between different intensity levels.
文章引用：梅柏威. 集成深度学习模型在地震烈度预测中的应用——以意大利数据为例[J]. 建模与仿真, 2025, 14(2): 619-630. https://doi.org/10.12677/mos.2025.142181

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