Design and realization of an early warning system for natural disaster on digital television in Indonesia

2012 ◽  
Author(s):  
Yuyu Wahyu ◽  
M.S.H. Shiddiq ◽  
Mashury Wahab
Keyword(s):  
Natural Disaster ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Digital Television

Special Issue on Early Warning for Natural Disaster Mitigation

2009 ◽  
Vol 4 (4) ◽  
pp. 529-529
Author(s):  
Masato Motosaka
Keyword(s):  
Research And Development ◽  
Natural Disaster ◽  
Earthquake Engineering ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Academic Community ◽  
Disaster Mitigation ◽  
Observation Data ◽  
Special Issue

Japan and many other counties face the risk of the natural disaster such as earthquakes, tsunamis, and floods. Natural disaster mitigation research and development are providing important, practical applications based on the development of the scientific technology. One major contribution is early warning system, being backed by observation and communication technology progress. Early warning research and development have been extensively studied domestically and internationally. Specifically, recent developments in earthquake engineering research and construction of seismic dense network have made it possible to issue earthquake warnings before the arrival of severe shaking. Such warnings enable emergency measures to be taken to protect lives, buildings, infrastructure, and transport from earthquake depredations. One such system went into practical use nationwide in Japan starting on October 1, 2007. Development has been conducted with cooperation of government, academic community and non-government, and private organizations. This special issue features papers on the early warning system for the natural disastermitigation covering issues ranging from natural science to social science. The recent developed earthquake early warning technology and its applications will be introduced. Besides earthquakes, the recent early warning technology for tsunami and flood are also included in this issue. The warning time available for tsunami and flood is much longer than that for earthquakes, and the contribution of numerical calculation using the real-time observation data differs with the type of disaster. Finally I would like to express my deepest gratitude for anonymous reviewers of papers in this special issue.

scholarly journals Research on Natural Disaster Early Warning System Based on UAV Technology

2021 ◽  
Vol 787 (1) ◽  
pp. 012084
Author(s):  
Bei Wu ◽  
Ruyu Fu ◽  
Jundao Chen ◽  
Junhui Zhu ◽  
Rongfang Gao
Keyword(s):  
Natural Disaster ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System

scholarly journals Developing A Group Model Building Causal Loop Diagram to Understand Millennial Generation Trust in Twitter as Early Warning System for Natural Disaster

2021 ◽  
Vol 23 (1) ◽  
pp. 35-46
Author(s):  
Hilya Mudrika Arini ◽  
Nurul Lathifah ◽  
Fina Ananda
Keyword(s):  
Natural Disaster ◽  
Early Warning ◽  
Millennial Generation ◽  
Early Warning System ◽  
Model Building ◽  
Warning System ◽  
Group Model ◽  
Causal Loop Diagram ◽  
Group Model Building ◽  
Twitter Users

Nowadays, Twitter is used as an Early Warning System (EWS) for disasters because of the speed and many users. Based on Asosiasi Penyedia Jasa Internet Indonesia (APJII) data, in 2017, almost 50% of internet users in Indonesia are born in 1983-1998. They are called the millennial generation. Therefore, this study aims to explore the trust of millennials towards Twitter as an EWS. This study utilizes the conceptual model from System Dynamics named Causal Loop Diagram (CLD) to identify the factors and the causal relationship among millennials' factors to trust Twitter as an EWS. It involves ten participants from the millennial generation, consisting of five passive Twitter users and five active Twitter users. A semi-structured interview was conducted with all participants to build the initial CLD gathered from each participant's perspective. Afterward, the initial CLD was verified by all participants through Focus Group Discussion. A group model building CLD that represents the influencing factors and their causal relationship of millennial generation trust in Twitter as EWS for a natural disaster is successfully developed from this study. The tweet frequency, the number of followers, the account credibility, the verified account, the level of trust in social media, and the content quality are considered the underlying factors of active and passive users to trust in Twitter as an EWS natural disaster.

PENERAPAN ALGORITMA CLUSTERING DALAM MENGELOMPOKKAN BANYAKNYA DESA/KELURAHAN MENURUT UPAYA ANTISIPASI/ MITIGASI BENCANA ALAM MENURUT PROVINSI DENGAN K-MEANS

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Mhd Gading Sadewo ◽  
Agus Perdana Windarto ◽  
Anjar Wanto
Keyword(s):  
Natural Disaster ◽  
Early Warning ◽  
Early Warning System ◽  
Clustering Algorithm ◽  
Warning System ◽  
Volcanic Eruptions ◽  
Disaster Mitigation ◽  
Assessment Process ◽  
The Government ◽  
The Village

Natural disasters are natural events that have a large impact on the human population. Located on the Pacific Ring of Fire (an area with many tectonic activities), Indonesia must continue to face the risk of volcanic eruptions, earthquakes, floods, tsunamis. Application of Clustering Algorithm in Grouping the Number of Villages / Villages According to Anticipatory / Natural Disaster Mitigation Efforts by Province With K-Means. The source of this research data is collected based on documents that contain the number of villages / kelurahan according to natural disaster mitigation / mitigation efforts produced by the National Statistics Agency. The data used in this study is provincial data consisting of 34 provinces. There are 4 variables used, namely the Natural Disaster Early Warning System, Tsunami Early Warning System, Safety Equipment, Evacuation Line. The data will be processed by clustering in 3 clushter, namely clusther high level of anticipation / mitigation, clusters of moderate anticipation / mitigation levels and low anticipation / mitigation levels. The results obtained from the assessment process are based on the Village / Kelurahan index according to the Natural Disaster Anticipation / Mitigation Efforts with 3 provinces of high anticipation / mitigation levels, namely West Java, Central Java, East Java, 9 provinces of moderate anticipation / mitigation, and 22 other provinces including low anticipation / mitigation. This can be an input to the government, the provinces that are of greater concern to the Village / Village According to the Natural Health Disaster Mitigation / Mitigation Efforts based on the cluster that has been carried out.Keywords: Data Mining, Natural Disaster, Clustering, K-Means

Sign in / Sign up

Export Citation Format

Share Document