{"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.
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.