A New Algorithm for Earthquake Prediction Using Machine Learning Methods

N. Jarah, Abbas Hanon Hassin Alasadi, K. M. Hashim
{"title":"A New Algorithm for Earthquake Prediction Using Machine Learning Methods","authors":"N. Jarah, Abbas Hanon Hassin Alasadi, K. M. Hashim","doi":"10.3844/jcssp.2024.150.156","DOIUrl":null,"url":null,"abstract":": Seismic tremors are among the foremost perilous normal fiascos individuals confront due to their event without earlier caution and their effect on their lives and properties. In expansion, to consider future disaster prevention measures for major earthquakes, it is necessary to predict earthquakes using Neural Networks (NN). A machine learning technique has developed a technology to predict earthquakes from ground controller data by measuring ground vibration and transmitting data by a sensor network. Devices to process this data and record it in a catalog of seismic data from 1900-2019 for Iraq and neighboring regions, then divide this data into 80% training data and 20% test data. It gave better results than other prediction algorithms, where the NN model performs better Seismic prediction than other machine learning methods.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3844/jcssp.2024.150.156","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

: Seismic tremors are among the foremost perilous normal fiascos individuals confront due to their event without earlier caution and their effect on their lives and properties. In expansion, to consider future disaster prevention measures for major earthquakes, it is necessary to predict earthquakes using Neural Networks (NN). A machine learning technique has developed a technology to predict earthquakes from ground controller data by measuring ground vibration and transmitting data by a sensor network. Devices to process this data and record it in a catalog of seismic data from 1900-2019 for Iraq and neighboring regions, then divide this data into 80% training data and 20% test data. It gave better results than other prediction algorithms, where the NN model performs better Seismic prediction than other machine learning methods.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用机器学习方法预测地震的新算法
:地震是人类面临的最危险的正常灾难之一,因为地震发生时没有提前采取预防措施,而且地震会对人们的生命和财产造成影响。在扩展过程中,为了考虑未来针对大地震的防灾措施,有必要使用神经网络(NN)预测地震。机器学习技术通过测量地面振动并通过传感器网络传输数据,开发了一种从地面控制器数据预测地震的技术。设备对这些数据进行处理,并将其记录在伊拉克及邻近地区 1900-2019 年的地震数据目录中,然后将这些数据分为 80% 的训练数据和 20% 的测试数据。与其他预测算法相比,NN 模型的地震预测效果更好。
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 去求助
来源期刊
Journal of Computer Science
Journal of Computer Science Computer Science-Computer Networks and Communications
CiteScore
1.70
自引率
0.00%
发文量
92
期刊介绍: Journal of Computer Science is aimed to publish research articles on theoretical foundations of information and computation, and of practical techniques for their implementation and application in computer systems. JCS updated twelve times a year and is a peer reviewed journal covers the latest and most compelling research of the time.
期刊最新文献
Features of the Security System Development of a Computer Telecommunication Network Performance Assessment of CPU Scheduling Algorithms: A Scenario-Based Approach with FCFS, RR, and SJF Website-Based Educational Application to Help MSMEs in Indonesia Develop A Multi-Split Cross-Strategy for Enhancing Machine Learning Algorithms Prediction Results with Data Generated by Conditional Generative Adversarial Network Improving the Detection of Mask-Wearing Mistakes by Deep Learning
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1