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.

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 Feasibility study of earthquake early warning in Tehran, Iran

2021 ◽  
Author(s):  
S. Enferadi ◽  
Z. H. Shomali ◽  
A. Niksejel
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Earthquake Early Warning ◽  
Disaster Mitigation ◽  
Lead Times ◽  
Worst Case ◽  
Earthquake Parameters ◽  
Earthquake Early Warning System ◽  
Earthquake Scenarios

AbstractIn this study, we examine the scientific feasibility of an Earthquake Early Warning System in Tehran, Iran, by the integration of the Tehran Disaster Mitigation and Management Organization (TDMMO) accelerometric network and the PRobabilistic and Evolutionary early warning SysTem (PRESTo). To evaluate the performance of the TDMMO-PRESTo system in providing the reliable estimations of earthquake parameters and the available lead-times for The Metropolis of Tehran, two different approaches were analyzed in this work. The first approach was assessed by applying the PRESTo algorithms on waveforms from 11 moderate instrumental earthquakes that occurred in the vicinity of Tehran during the period 2009–2020. Moreover, we conducted a simulation analysis using synthetic waveforms of 10 large historical earthquakes that occurred in the vicinity of Tehran. We demonstrated that the six worst-case earthquake scenarios can be considered for The Metropolis of Tehran, which are mostly related to the historical and instrumental events that occurred in the southern, eastern, and western parts of Tehran. Our results indicate that the TDMMO-PRESTo system could provide reliable and sufficient lead-times of about 1 to 15s and maximum lead-times of about 20s for civil protection purposes in The Metropolis of Tehran.

scholarly journals RISIKO KAWASAN LONGSOR DALAM UPAYA MITIGASI BENCANA MENGGUNAKAN SISTEM INFORMASI GEOGRAFIS

Pondasi ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 67
Author(s):  
Fakhryza Nabila Hamida ◽  
Hasti Widyasamratri
Keyword(s):  
Land Use ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Disaster Risk ◽  
Risk Mapping ◽  
Disaster Mitigation ◽  
Accurate Information ◽  
Loss Of Life ◽  
Landslide Disaster

ABSTRACTIndonesia is an area prone to landslides. The occurrence of this landslide disaster can cause a large impact such as damage and loss both material and non-material. The availability of complete and accurate information in controlling land use in landslide prone areas in the development of an area becomes very important in minimizing the loss of life and losses, both physical, social and economic. This information must be disseminated to the community as an early warning system in disaster mitigation efforts. Identification of the characteristics of landslide prone areas requires a risk mapping of landslide prone areas in efforts to mitigate disasters can be done using Geographic Information Systems (GIS). The results in this study indicate the need to identify disaster risk in detail because basically, an area threatened by disaster does not necessarily mean that each community has the same level of disaster risk. Mapping can be done by clustering or by identifying each building in a vulnerable area based on the level of risk of landslides. Keywords: risk analysis, landslides, disaster mitigation, GIS ABSTRAKIndonesia merupakan wilayah yang rawan terhadap bencana longsor. Terjadinya bencana longsor ini dapat menyebabkan dampak yang besar seperti kerusakan dan kerugian baik materiil maupun non materiil. Tersedianya informasi yang lengkap dan akurat dalam pengendalian pemanfaatan lahan di kawasan rawan bencana longsor dalam pengembangan suatu wilayah menjadi hal yang sangat penting dalam meminimalisir adanya korban jiwa dan kerugian-kerugian baik fisik, sosial maupun ekonomi. Informasi tersebut harus disebarkan kepada masyarakat sebagai sistem peringatan dini dalam upaya mitigasi bencana. Identifikasi karakteristik daerah rawan longsor diperlukan sebuah pemetaan risiko kawasan rawan longsor dalam upaya mitigasi bencana dapat dilakukan menggunakan Sistem Informasi Geografis (SIG). Hasil dalam penelitian ini menunjukkan perlunya identifikasi risiko bencana secara detail karena pada dasarnya, suatu kawasan yang terancam bencana belum tentu tiap masyarakatnya mempunyai tingkat risiko bencana yang sama. Pemetaan dapat dilakukan dengan pengklusteran maupun dengan identifikasi setiap bangunan dalam kawasan rawan berdasarkan tingkat risiko terhadap bencana tanah longsor.Kata Kunci: analisis risiko, tanah longsor, mitigasi bencana, GIS

scholarly journals Design and Implementation of Real-Time Flood Early Warning System (FEWS) Based on IoT Blynk Application

ELKHA ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 113
Author(s):  
Hasbi Nur Prasetyo Wisudawan
Keyword(s):  
Water Level ◽  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Disaster Risk ◽  
Disaster Mitigation ◽  
Average Temperature ◽  
Flood Warning ◽  
Flood Warning System

Disaster occurrence in Indonesia needs attention and role from all parties including the community to reduce the risks.  Disaster mitigation is one of the ways to reduce the disaster risk through awareness, capacity building, and the development of physical facilities, for example by applying disaster mitigation technology (early warning system, EWS). EWS is one of the effective methods to minimize losses due to disasters by providing warning based on certain parameters for disasters which usually occur such as floods. This research promotes a real-time IoT-based EWS flood warning system (Flood Early Warning System, FEWS) using Arduino and Blynk as well as Global System for Mobile Communication network (GSM) as the communication medium. The steps for implementing FEWS system in real locations are also discussed in this paper. Parameters such as water level, temperature, and humidity as well as rain conditions that are read by the EWS sensor can be accessed in real-time by using android based Blynk application that has been created. The result of the measurement of average temperature, humidity, and water level were 28.6 oC, 63.7 %, and 54.5 cm. Based on this analysis, the parameters indicated that the water level is in normal condition and there are no signs indicating that there will be flooding in the 30 days observation.  Based on the data collected by the sensor, FEWS can report four conditions, namely Normal, Waspada Banjir (Advisory), Siaga Banjir (Watch), and Awas Banjir (Warning) that will be sent immediately to the Blynk FEWS application user that has been created.

scholarly journals The use of rapid assessment for flood hazard map development in upper citarum river basin

2018 ◽  
Vol 229 ◽  
pp. 04011
Author(s):  
Idham Riyando Moe ◽  
Akbar Rizaldi ◽  
Mohammad Farid ◽  
Arie Setiadi Moerwanto ◽  
Arno Adi Kuntoro
Keyword(s):  
River Basin ◽  
Early Warning ◽  
Early Warning System ◽  
Flood Hazard ◽  
Rapid Assessment ◽  
Warning System ◽  
Observation Data ◽  
Hazard Map ◽  
Material Loss ◽  
Flood Hazard Map

Flood is a natural disaster that can occur at any time and anywhere. The flood disaster causes material and non-material loss, then in order to increase the resilience to disaster, an early warning system is needed. The data is indispensable as a reference to make an early warning system. Unfortunately, flood assessment in purpose to record the data is often conducted much later after the event occurs. Therefore, this research was conducted to do modelling of flood hazard map is quantitatively and validated with observation data as a form of rapid flood assessment. The location of this study is in the Upper Citarum River Basin, around Bandung basin. The model is well done if the result shows the location of the flood as illustrated as the observational data. The result shows fair agreement with observed data where some points of inundated areas are captured and the location of inundated areas from modelling result looks similar to the inundated area from observation data.

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