MULTIPLE EVALUATION OF URBAN SHRINKING WITH MITIGATING THE LANDSLIDE DISASTER RISK —DESIGNING AND EVALUATING THE SCENARIO IN HIROSHIMA CITY, HIROSHIMA PREFECTURE—

Author(s):  
Shota TAMURA ◽  
Takahiro TANAKA
Pondasi ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 67
Author(s):  
Fakhryza Nabila Hamida ◽  
Hasti Widyasamratri

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


2015 ◽  
Vol 77 (1) ◽  
Author(s):  
Hamzah Hussin ◽  
Sarah Aziz Abdul Ghani ◽  
Tajul Anuar Jamaluddin ◽  
Mohammad Khairul Azhar Abdul Razab

Landslide is a natural process that is common in hilly areas, whether natural hills or areas that have been disturbed by human activity. Landslide is a type of geological hazard that become an issue and often gets attention at all levels of society. Increased of landslide cases in Malaysia and generate varies problems of social, economic, technical and legal cause a specific and precise definition of landslide to be accepted by all stakeholders in the country to allow a comprehensive landslide disaster risk management action to be created. Problems arise among scientists, professionals and other stakeholders to use the agreed upon of a geohazard and geodisaster term when translated from English. This paper discusses the acceptable definition and terms of the "geohazard" landslides in the context of Malaysia.


2016 ◽  
Vol 4 (4) ◽  
Author(s):  
Hironaga Akita ◽  
Zen-ichiro Kimura ◽  
Mohd Zulkhairi Mohd Yusoff ◽  
Nobutaka Nakashima ◽  
Tamotsu Hoshino

Burkholderia sp. strain CCA53 was isolated from leaf soil collected in Higashi-Hiroshima City in Hiroshima Prefecture, Japan. Here, we present a draft genome sequence of this strain, which consists of a total of 4 contigs containing 6,647,893 bp, with a G+C content of 67.0% and comprising 9,329 predicted coding sequences.


2017 ◽  
Vol 24 ◽  
pp. 326-331 ◽  
Author(s):  
Sowedi Masaba ◽  
David N. Mungai ◽  
Moses Isabirye ◽  
Haroonah Nsubuga

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