• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing >Spatial analisys of magnitude distribution for earthquake prediction using neural network based on automatic clustering in Indonesia
【24h】

Spatial analisys of magnitude distribution for earthquake prediction using neural network based on automatic clustering in Indonesia

机译：印度尼西亚基于自动聚类的神经网络震级分布的空间分析

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

A spatial analysis of magnitude distribution is presented in this paper to identify the optimal number of clusters based on seismic data of all region in Indonesia. The data were obtained from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and United States Geological Survey's (USGS). Clustering process consist of two steps: finding the global optimum number of clusters using Valley Tracing and clustering the dataset based on Hierarchical K-means. The optimal number of cluster obtained is 6 cluster. A model of Artificial Neural Networks (ANNs) is implemented for selected cluster to conduct an earthquake prediction. The architecture of the neural network model is composed of seven inputs, two hidden layers with thirty-two nodes each and one output. Back propagation training method and sigmoid activation function are applied. The input values are related to the b-value, the Bath's law, and the Omori-Utsu's law. The ANNs prototype predicts earthquake which is equal or larger than the given threshold magnitude during the next five days after an earthquake occurrence. Statistical tests are provided using two threshold values (5.5 and 6). The ANNs result showed that the proposed model gave better performance to predict earthquake that equal or larger than 6 Richter's scale magnitude. Finally, the result were compared to other ANNs model showing quantitatively and qualitatively better results.
机译：本文对震级分布进行了空间分析，以根据印度尼西亚所有地区的地震数据确定最佳的星团数。数据来自印度尼西亚气象，气候和地球物理机构（BMKG）和美国地质调查局（USGS）。聚类过程包括两个步骤：使用Valley Tracing查找全局最优聚类数量，以及基于层次K均值聚类数据集。获得的最佳群集数为6个群集。针对所选集群实施了人工神经网络（ANN）模型，以进行地震预测。神经网络模型的体系结构由七个输入，两个隐藏层（每个隐藏层各有32个节点和一个输出）组成。应用了反向传播训练方法和S形激活函数。输入值与b值，巴斯定律和大森宇津定律有关。 ANNs原型预测地震发生后的五天内，地震将等于或大于给定的阈值震级。使用两个阈值（5.5和6）提供统计检验。人工神经网络的结果表明，所提出的模型可以更好地预测等于或大于6里氏震级的地震。最后，将结果与其他ANN模型进行比较，从数量和质量上显示出更好的结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号