Modeling of Information Flow for Early Warning in Mount Merapi Area, Indonesia

2016 ◽  
Vol 11 (1) ◽  
pp. 60-71 ◽  
Author(s):  
Leslie Jamie Cobar ◽  
◽  
Djoko Legono ◽  
Kuniaki Miyamoto ◽  
Keyword(s):  
Information Flow ◽  
Early Warning ◽  
Early Warning System ◽  
Information Transfer ◽  
Data Transfer ◽  
Warning System ◽  
Volcanic Eruptions ◽  
Flow Network ◽  
The Public ◽  
Flow Through

Indonesia’s Mount Merapi is one of the world’s most active, dangerous volcanoes. Its 2010 eruption – the largest following the 20th century – and succeeding 2011 lahar events killed 389 persons and injured and displaced many more. One way to mitigate a disaster’s impact is the provision of reliable information to the public through a well-established early warning system (EWS). A well-managed information flow network is the key to delivering early warning information, however, there is a lack of understanding on the information transfer down to the citizens. In addition, implementing the 2007 disaster management law may have affected Merapi’s EWS. This study reinvestigates Merapi’s EWS information flow through the construction of an information flow network. A single information flow network was difficult to construct due to the inconsistency of structures per district. Different networks had to be constructed for volcanic eruptions and lahars in each district. Inconsistencies were also found in the roles of the agencies that determine when evacuation orders would be issued. The system also had data transfer gaps and vulnerabilities such as redundancies, mistransfers and bottlenecks. Its use of forecasting information as a basis for decision-making must be reviewed for lahar information flow networks. Improving Merapi’s EWS must involve handling these issues.

scholarly journals Accelerating information of tsunami disaster using early warning system (EWS) Devices: smart solution for communication, navigation and surveillance due to tidal sea level

2021 ◽  
Vol 331 ◽  
pp. 07008
Author(s):  
Nurhadi Santosa ◽  
Nadjadji Anwar ◽  
Bangun Muljo Sukojo ◽  
Wesam Al Madhoun
Keyword(s):  
Sea Level ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
The Public ◽  
New Device ◽  
Tsunami Disaster ◽  
Tsunami Risk ◽  
The Government ◽  
Community Sector

Oneof the important aspects in reducing Tsunami Risk is understanding Natural Hazards. The loss of INA TEWS equipment worth hundreds of billions of rupiah and the occurrence of the Tsunami in Aceh and other parts of Indonesia are warnings that our society needs the help of effective early warning tools at affordable prices. Using this device will make it easier for the community to monitor natural events from the field and reduce disaster risk. Currently the government has purchased hundreds of units of JRC-UNESCO products, namely IDSL (Inexpensive Device for Sea Level), but this device takes 21 minutes of information to reach the community. Therefore, a new device that has been prepared is needed. (FIDELA EARLY WARNING SYSTEM/FEWS) is a tool that involves the Equipment sector, Operations sector and Community sector. The results shows that this device is that the time for sending information to the public is only 5 minutes.

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

scholarly journals Assessment of the warning system against floods on a rural area: the case of the lower Siret River (Romania)

2013 ◽  
Vol 13 (2) ◽  
pp. 409-416 ◽  
Author(s):  
F. Salit ◽  
L. Zaharia ◽  
G. Beltrando
Keyword(s):  
Risk Management ◽  
Rural Area ◽  
Early Warning ◽  
Flood Risk ◽  
Early Warning System ◽  
Warning System ◽  
Flood Risk Management ◽  
Event Tree ◽  
The Public ◽  
Risk Education

Abstract. The development of non-structural measures such as an early warning system, across the Europe, in flood risk management, requires a better understanding of the public involved and of the territory threatened. This paper aims to conduct an assessment of early warning and information to people with an analysis of the population's behaviour, presented in a form of an event tree. The objective is to understand the strengths and weaknesses of the warning system during a deadly flood in the lower Siret River (Romania) in 2005 and to demonstrate that each warning system has to be adapted to the territory in which it is effective. The behavioural model aims to determine to what extent the warning system can be improved but also to suggest ways to adapt risk education to the study area.

scholarly journals Presentation and preliminary evaluation of the operational Early Warning System in Cyprus

2009 ◽  
Vol 9 (4) ◽  
pp. 1213-1219 ◽  
Author(s):  
K. Savvidou ◽  
S. Michaelides ◽  
K. A. Nicolaides ◽  
P. Constantinides
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Preliminary Evaluation ◽  
The Public ◽  
Weather Model ◽  
Weather Forecasters ◽  
Weather Risk ◽  
Weather Stations ◽  
Local Weather

Abstract. The Cyprus Early Warning System (EWS) and its validation are presented in this study. The EWS was developed within the framework of the Weather Risk Reduction in the Mediterranean project (RiskMed), the main objective of which is to warn the authorities and the public for severe weather phenomena, in order to minimise the impacts of weather related hazards. For the validation of the EWS, a comparison is made between the output of the system and the observations retrieved from 24 automatic weather stations operated by the Meteorological Service of Cyprus. From the validation, it resulted that the system underestimates the temperatures and overestimates the rain and the wind over Cyprus. These results can be attributed, firstly to the sea coverage of the study areas and secondly to the weakness of the weather model to represent topography. The EWS is a useful forecasting tool for local weather forecasters whose duties include the issue of warnings which are subsequently disseminated to the appropriate authorities acting for the safety of people and properties.

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