The role of parameters in Bayesian Online Changepoint Detection: detecting early warning of mount Merapi eruptions

贝叶斯在线突变点检测中参数的作用:探测默拉皮火山喷发的早期预警

阅读:1

Abstract

Indonesia is a country that is surrounded by active volcanoes, which may erupt at any time; therefore, an online early warning system of volcanic eruption is crucial. In this paper, an online early warning system is constructed based on the changepoints detection on earthquake magnitude time series. This online early warning system is built using a Bayesian Online Changepoint Detection (BOCPD) method. One of the method's advantages is that one can customize the parameters (initial hyper-parameters and hazard-rate parameter) of BOCPD to follow a chosen constraint. These parameters determine the time and number of changepoints. An algorithm, called Appropriate Parameters of Bayesian Online Changepoint Detection for Early Warning (APBOCPD-EW), is proposed to get the parameters that lead the detection to the early warning points before eruption. We apply the algorithm for online early warning of mount Merapi eruptions. The results show that the proposed method produces parameters that give good estimation time for early warnings of mount Merapi's eruptions.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。