College of Civil Engineering, Dalian Minzu University, Dalian, 116600, Liaoning, China.
PLoS One. 2024 Feb 28;19(2):e0299639. doi: 10.1371/journal.pone.0299639. eCollection 2024.
This study aims to predict the significant duration (D5-75, D5-95) of seismic motion by employing machine learning algorithms. Based on three parameters (moment magnitude, fault distance, and average shear wave velocity), two additional parameters(fault top depth and epicenter mechanism parameters) were introduced in this study. The XGBoost algorithm is utilized for characteristic parameter optimization analysis to obtain the optimal combination of four parameters. We compare the prediction results of four machine learning algorithms (random forest, XGBoost, BP neural network, and SVM) and develop a new method of significant duration prediction by constructing two fusion models (stacking and weighted averaging). The fusion model demonstrates an improvement in prediction accuracy and generalization ability of the significant duration when compared to single algorithm models based on evaluation indicators and residual values. The accuracy and rationality of the fusion model are validated through comparison with existing research.
本研究旨在通过机器学习算法预测地震动的显著持续时间（D5-75、D5-95）。基于三个参数（矩震级、断层距离和平均剪切波速度），本研究引入了另外两个参数（断层顶深和震源机制参数）。XGBoost 算法用于特征参数优化分析，以获得四个参数的最优组合。我们比较了四种机器学习算法（随机森林、XGBoost、BP 神经网络和 SVM）的预测结果，并通过构建两个融合模型（堆叠和加权平均）开发了一种新的显著持续时间预测方法。基于评价指标和残差，融合模型在显著持续时间的预测精度和泛化能力方面优于单一算法模型。通过与现有研究的比较，验证了融合模型的准确性和合理性。
