Sholihat Seli Siti, Indratno Sapto Wahyu, Mukhaiyar Utriweni
Statistics Research Group, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung, 40132, Indonesia.
U-CoE AI-VLB (Artificial Intelligence for Vision, Natural Language Processing and Big Data Analytics), Indonesia.
Heliyon. 2021 Jul 15;7(7):e07482. doi: 10.1016/j.heliyon.2021.e07482. eCollection 2021 Jul.
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.
印度尼西亚是一个被活火山环绕的国家，这些火山随时可能喷发；因此，火山喷发在线预警系统至关重要。本文基于地震震级时间序列的变化点检测构建了一个在线预警系统。该在线预警系统使用贝叶斯在线变化点检测（BOCPD）方法构建。该方法的优点之一是可以根据所选约束定制BOCPD的参数（初始超参数和危险率参数）。这些参数决定了变化点的时间和数量。提出了一种名为“用于早期预警的贝叶斯在线变化点检测的合适参数（APBOCPD-EW）”的算法，以获取能使检测在火山喷发前到达预警点的参数。我们将该算法应用于默拉皮火山喷发的在线预警。结果表明，所提出的方法产生的参数能为默拉皮火山喷发的早期预警给出良好的估计时间。
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