摘要
由于地震区域地质结构的差异性,采用单一模型难以在分割后的所有小区域都达到较好的预测效果。为此,提出一种基于区域分割的混合型算法。分别利用反向传播神经网络算法和树突状细胞算法对分割后的区域进行建模,选择效果较好的算法作为该区域的预测算法。根据不同区域的权值来预测整个大区域的地震发生情况。实验结果表明,该算法能够有效改善地震预测的效果。
Due to the variations of geological structure in different earthquake regions,earthquake prediction in the sub-regions after dividing may not be well performed by a simple model.To solve this problem,a hybrid algorithm based on region segmentation is presented.After region segmentation,Back Propagation Neural Network (BPNN) algorithm and Dendritic Cell Algorithm(DCA) are respectively used to model each sub-region,and the better one is selected as the prediction algorithm of the sub-region.The earthquake occurrence situation in the whole region is calculated according to the weights of each sub-region.Experimental results show that the proposed algorithm can improve the effect of earthquake prediction.
作者
周天祥
董红斌
周雯
ZHOU Tianxiang;DONG Hongbin;ZHOU Wen(School of Cyber Science and Engineering,Wuhan University,Wuhan 430072,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2019年第9期310-315,共6页
Computer Engineering
基金
国家自然科学基金“计算机免疫智能的连续应答机制及其应用研究”(61877045)
关键词
树突状细胞算法
反向传播神经网络
混合算法
区域分割
地震预测
Dendritic Cell Algorithm(DCA)
Back Propagation Neural Network(BPNN)
hybrid algorithm
region segmentation
earthquake prediction
