刘新,尹康达,寇海川. 基于GA-GRNN的地震震级预测模型[J]. 华北地震科学,2023, 41(2):37-42. doi:10.3969/j.issn.1003−1375.2023.02.006.
引用本文: 刘新,尹康达,寇海川. 基于GA-GRNN的地震震级预测模型[J]. 华北地震科学,2023, 41(2):37-42. doi:10.3969/j.issn.1003−1375.2023.02.006.
LIU Xin,YIN Kangda,KOU Haichuan. The Prediction Model of Earthquake Magnitude Based on GA-GRNN[J]. North China Earthquake Sciences,2023, 41(2):37-42. doi:10.3969/j.issn.1003−1375.2023.02.006.
Citation: LIU Xin,YIN Kangda,KOU Haichuan. The Prediction Model of Earthquake Magnitude Based on GA-GRNN[J]. North China Earthquake Sciences,2023, 41(2):37-42. doi:10.3969/j.issn.1003−1375.2023.02.006.

基于GA-GRNN的地震震级预测模型

The Prediction Model of Earthquake Magnitude Based on GA-GRNN

  • 摘要: 为科学描述地震震级与其敏感因子之间复杂的非线性关系,将遗传算法(Genetic Algorithm,GA)与广义回归神经网络(General Regression Neural Network,GRNN)相结合,利用主成分分析法(Principal Component Analysis,PCA)对地震震级敏感因子进行降维处理,然后对提取出的主成分进行归一化,将归一化的主成分数据作为预测模型的输入向量,地震震级作为预测模型的输出向量;以20个地震数据作为学习样本进行训练,运用GA寻优获得最优光滑因子,建立基于PCA-GA-GRNN的地震震级预测模型,并对8个测试样本进行预测。结果表明:PCA-GA-GRNN模型的最小误差、最大误差和平均误差分别为1.563 0%、4.878 0%和2.647 0%,其平均误差相比于GA-GRNN模型和GRNN模型分别降低5.666 7%和5.026 4%,具有较高的预测精度。

     

    Abstract: In order to describe the complex nonlinear relationship between earthquake magnitude and its sensitive factor, the genetic algorithm ( GA ) combined with general regression neural network ( GRNN ), and the principal component analysis ( PCA ) were used to reduce the dimension of earthquake sensitive factor. Then normalizing the extracted principal components which were used as input vectors of general regression neural network, and earthquake magnitude was used as output vector, 20 typical cases of earthquake magnitude were used for training data, then using GA to optimize the best smooth factor, finally the prediction model for earthquake magnitude based on PCA-GA-GRNN was established, and it was used to predict 8 prediction samples. The result shows that the minimum error, maximum error and average error of PCA-GA-GRNN model were 51.5630%、4.8780% and 2.647% respectively. Compared with GSM-GRNN model and GRNN model, the average error of PCA-GA-GRNN model is reduced by 5.6667% and 5.0264%, respectively. Therefore, PCA-GA-GRNN model has higher prediction accuracy.

     

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