{"title":"利用聚类分层聚类方法对东南亚地区地震事件点进行聚类","authors":"Adi Arifin","doi":"10.32520/stmsi.v12i3.3150","DOIUrl":null,"url":null,"abstract":"Earthquake is an inevitable and highly dangerous natural disaster due to its sudden and uncontrollable occurrence. Earthquakes frequently happen in the Southeast Asia region, resulting in infrastructure damage, loss of life, and economic disruption. Therefore, efforts should be made to mitigate earthquake risks, including the classification of earthquake data in the Southeast Asia region. The data used in this study were obtained from The Seismological Facility for the Advancement of Geoscience (SAGE), a facility funded by NSF and operated by the Incorporated Research Institutions for Seismology (IRIS). The study employed Agglomerative Hierarchical Clustering (AHC) to group the data into multiple clusters. The validity of the formed clusters was assessed using Silhouette Score Index (SSI), Davies Doublin Index (DBI), and Calinski Harabasz Index (CHI). The study involved two clustering processes, resulting in three clusters for the first clustering process aimed at creating new attributes, namely Area, and three clusters for the second clustering process aimed at identifying the types of earthquakes in the Southeast Asia region. These three formed clusters had SSI, DBI, and CHI values of 0.434353, 0.887791, and 3769.030146, respectively, indicating that AHC successfully classified the earthquake data. The findings of this research serve as a reference for further studies such as earthquake prediction and contribute to disaster mitigation strategies to enhance preparedness for future events.","PeriodicalId":32357,"journal":{"name":"Jurnal Sistem Informasi","volume":"40 30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clustering Earthquake Event Points in the Southeast Asia Region using Agglomerative Hierarchical Clustering\",\"authors\":\"Adi Arifin\",\"doi\":\"10.32520/stmsi.v12i3.3150\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Earthquake is an inevitable and highly dangerous natural disaster due to its sudden and uncontrollable occurrence. Earthquakes frequently happen in the Southeast Asia region, resulting in infrastructure damage, loss of life, and economic disruption. Therefore, efforts should be made to mitigate earthquake risks, including the classification of earthquake data in the Southeast Asia region. The data used in this study were obtained from The Seismological Facility for the Advancement of Geoscience (SAGE), a facility funded by NSF and operated by the Incorporated Research Institutions for Seismology (IRIS). The study employed Agglomerative Hierarchical Clustering (AHC) to group the data into multiple clusters. The validity of the formed clusters was assessed using Silhouette Score Index (SSI), Davies Doublin Index (DBI), and Calinski Harabasz Index (CHI). The study involved two clustering processes, resulting in three clusters for the first clustering process aimed at creating new attributes, namely Area, and three clusters for the second clustering process aimed at identifying the types of earthquakes in the Southeast Asia region. These three formed clusters had SSI, DBI, and CHI values of 0.434353, 0.887791, and 3769.030146, respectively, indicating that AHC successfully classified the earthquake data. The findings of this research serve as a reference for further studies such as earthquake prediction and contribute to disaster mitigation strategies to enhance preparedness for future events.\",\"PeriodicalId\":32357,\"journal\":{\"name\":\"Jurnal Sistem Informasi\",\"volume\":\"40 30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Jurnal Sistem Informasi\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32520/stmsi.v12i3.3150\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jurnal Sistem Informasi","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32520/stmsi.v12i3.3150","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
地震是一种不可避免的、危险性高的自然灾害,具有突发性和不可控性。东南亚地区经常发生地震,导致基础设施受损、人员伤亡和经济中断。因此,应努力减轻地震风险,包括对东南亚地区的地震数据进行分类。本研究中使用的数据来自地球科学进步地震设施(SAGE),该设施由美国国家科学基金会资助,由地震学联合研究机构(IRIS)运营。本研究采用聚类分层聚类(AHC)将数据分成多个聚类。采用Silhouette Score Index (SSI)、Davies Doublin Index (DBI)和Calinski Harabasz Index (CHI)评价聚类的有效性。该研究涉及两个聚类过程,第一个聚类过程有三个聚类,目的是创建新的属性,即面积,第二个聚类过程有三个聚类,目的是识别东南亚地区的地震类型。这三个聚类的SSI值为0.434353,DBI值为0.887791,CHI值为3769.030146,说明AHC对地震数据分类成功。这项研究的结果可作为地震预测等进一步研究的参考,并有助于制定减灾战略,以加强对未来事件的准备。
Clustering Earthquake Event Points in the Southeast Asia Region using Agglomerative Hierarchical Clustering
Earthquake is an inevitable and highly dangerous natural disaster due to its sudden and uncontrollable occurrence. Earthquakes frequently happen in the Southeast Asia region, resulting in infrastructure damage, loss of life, and economic disruption. Therefore, efforts should be made to mitigate earthquake risks, including the classification of earthquake data in the Southeast Asia region. The data used in this study were obtained from The Seismological Facility for the Advancement of Geoscience (SAGE), a facility funded by NSF and operated by the Incorporated Research Institutions for Seismology (IRIS). The study employed Agglomerative Hierarchical Clustering (AHC) to group the data into multiple clusters. The validity of the formed clusters was assessed using Silhouette Score Index (SSI), Davies Doublin Index (DBI), and Calinski Harabasz Index (CHI). The study involved two clustering processes, resulting in three clusters for the first clustering process aimed at creating new attributes, namely Area, and three clusters for the second clustering process aimed at identifying the types of earthquakes in the Southeast Asia region. These three formed clusters had SSI, DBI, and CHI values of 0.434353, 0.887791, and 3769.030146, respectively, indicating that AHC successfully classified the earthquake data. The findings of this research serve as a reference for further studies such as earthquake prediction and contribute to disaster mitigation strategies to enhance preparedness for future events.