Tsunami Early Warning Detection using Bayesian Classifier

Liliana, DY; Priharsari, D

Tsunami Early Warning Detection using Bayesian Classifier

Liliana, DY Priharsari, D

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2019 2nd International Conference of Computer and Informatics Engineering (IC2IE), 2019
Issue Date:: 2019-09

Closed Access

	Filename	Description	Size
	08940823.pdf	Published version	168.62 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Liliana, DY
dc.contributor.author	Priharsari, D
dc.date	2019-09-10
dc.date.accessioned	2020-06-19T01:30:59Z
dc.date.available	2020-06-19T01:30:59Z
dc.date.issued	2019-09
dc.identifier.citation	2019 2nd International Conference of Computer and Informatics Engineering (IC2IE), 2019
dc.identifier.isbn	978-1-7281-2384-4
dc.identifier.uri	http://hdl.handle.net/10453/141558
dc.description.abstract	Tsunami is a term for disaster where the seawater rises to the land caused by the great earthquake with a shallow epicenter in the ocean. Indonesia is vulnerable to the tsunami; especially in the area where the junction of Eurasia, Indo-Australia, and Pacific Plates meet. Soon after the earthquake happened, there is a time interval before a tsunami to come. The break-time can be used to alert people to evacuate themselves from the Tsunami. This study proposed a tsunami early warning system, which autonomously predicts the potential of tsunami hazard using machine learning techniques. Bayesian classifier was trained to predict the tsunami potential. The tsunami training data was taken from the InaTews website; a national project of Indonesia that involves many institutions, local or international. InaTews informed the real-time data of earthquakes and tsunami that happened in Indonesia. The proposed methods extracted some invariant characteristics from the data and trained the machine learning classifiers to predict the potential result; either tsunami or not-tsunami using three parameters of earthquake: magnitude, epicenter, and location. The experiment gave an optimistic result; using a varying number of training data, it gained 92.37% on the average accuracy rate and 0.98 on the F1 score.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2019 2nd International Conference of Computer and Informatics Engineering (IC2IE)
dc.relation.ispartof	International Conference of Computer and Informatics Engineering
dc.relation.isbasedon	10.1109/ic2ie47452.2019.8940823
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Tsunami Early Warning Detection using Bayesian Classifier
dc.type	Conference Proceeding
utslib.location.activity	Banyuwangi, Indonesia
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-19T01:30:53Z
pubs.finish-date	2019-09-11
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-09-10
dc.location	Piscataway, USA

Abstract:

Tsunami is a term for disaster where the seawater rises to the land caused by the great earthquake with a shallow epicenter in the ocean. Indonesia is vulnerable to the tsunami; especially in the area where the junction of Eurasia, Indo-Australia, and Pacific Plates meet. Soon after the earthquake happened, there is a time interval before a tsunami to come. The break-time can be used to alert people to evacuate themselves from the Tsunami. This study proposed a tsunami early warning system, which autonomously predicts the potential of tsunami hazard using machine learning techniques. Bayesian classifier was trained to predict the tsunami potential. The tsunami training data was taken from the InaTews website; a national project of Indonesia that involves many institutions, local or international. InaTews informed the real-time data of earthquakes and tsunami that happened in Indonesia. The proposed methods extracted some invariant characteristics from the data and trained the machine learning classifiers to predict the potential result; either tsunami or not-tsunami using three parameters of earthquake: magnitude, epicenter, and location. The experiment gave an optimistic result; using a varying number of training data, it gained 92.37% on the average accuracy rate and 0.98 on the F1 score.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/141558