EARTHQUAKE PREDICTION MODEL BASED ON DANGER THEORY IN ARTIFICIAL IMMUNITY

IF 0.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Wen Zhou, Yiwen Liang, Zhe Ming, Hongbin Dong
{"title":"EARTHQUAKE PREDICTION MODEL BASED ON DANGER THEORY IN ARTIFICIAL IMMUNITY","authors":"Wen Zhou, Yiwen Liang, Zhe Ming, Hongbin Dong","doi":"10.14311/nnw.2020.30.016","DOIUrl":null,"url":null,"abstract":"Earthquake prediction is an extraordinarily stochastic process. Determining the occurrence time, location of epicenter and magnitude of a coming earthquake in the following month is an extremely difficult task. Nowadays, some geophysical, statistical and machine learning methods are adopted to predict earthquakes, however, for the insufficient medium-large seismic data, their results are not satisfactory. Due to there is no obvious empirical relationship between seismicity features, magnitude and location of a coming earthquake in a particular time window, an earthquake prediction approach based on danger theory is proposed in this paper. It extracts eight indicators calculated from earthquake data for recent years in Sichuan and surroundings by Gutenberg-Richter(GR) inverse power-law, and predicts quakes with magnitude lager than 4.5 during the following month by numerical differential based Dendritic Cell Algorithm (ndDCA). We compare this approach with six state-of-art earthquake prediction algorithms. Overall our algorithm yields the encouraging results in all the qualified parameters assessed, and it provides technical support for the application of earthquake prediction.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"38 1","pages":"231-247"},"PeriodicalIF":0.7000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Network World","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.14311/nnw.2020.30.016","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 5

Abstract

Earthquake prediction is an extraordinarily stochastic process. Determining the occurrence time, location of epicenter and magnitude of a coming earthquake in the following month is an extremely difficult task. Nowadays, some geophysical, statistical and machine learning methods are adopted to predict earthquakes, however, for the insufficient medium-large seismic data, their results are not satisfactory. Due to there is no obvious empirical relationship between seismicity features, magnitude and location of a coming earthquake in a particular time window, an earthquake prediction approach based on danger theory is proposed in this paper. It extracts eight indicators calculated from earthquake data for recent years in Sichuan and surroundings by Gutenberg-Richter(GR) inverse power-law, and predicts quakes with magnitude lager than 4.5 during the following month by numerical differential based Dendritic Cell Algorithm (ndDCA). We compare this approach with six state-of-art earthquake prediction algorithms. Overall our algorithm yields the encouraging results in all the qualified parameters assessed, and it provides technical support for the application of earthquake prediction.
基于人工免疫危险理论的地震预测模型
地震预报是一个非常随机的过程。确定接下来一个月即将发生的地震的发生时间、震中位置和震级是一项极其困难的任务。目前,一些地球物理、统计和机器学习等方法用于地震预测,但由于中大型地震数据不足,预测结果并不理想。针对某一特定时间窗内即将发生地震的地震活动性特征、震级和位置之间没有明显的经验关系,本文提出了一种基于危险理论的地震预测方法。利用古腾堡-里希特(GR)逆幂律法提取四川及周边地区近年地震资料计算出的8个指标,利用基于数值差分的树突状细胞算法(ndDCA)预测未来一个月震级在4.5级以上的地震。我们将这种方法与六种最先进的地震预测算法进行比较。总的来说,我们的算法在所有合格的参数评估中都取得了令人鼓舞的结果,为地震预测的应用提供了技术支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Neural Network World
Neural Network World 工程技术-计算机:人工智能
CiteScore
1.80
自引率
0.00%
发文量
0
审稿时长
12 months
期刊介绍: Neural Network World is a bimonthly journal providing the latest developments in the field of informatics with attention mainly devoted to the problems of: brain science, theory and applications of neural networks (both artificial and natural), fuzzy-neural systems, methods and applications of evolutionary algorithms, methods of parallel and mass-parallel computing, problems of soft-computing, methods of artificial intelligence.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信