| Autor: |
Maharani, Yohana Noradika, Nugroho, Arif Rianto Budi, Adiba, Dzikrina Farah, Sulistiyowati, Iin, Prasetya, Johan Danu, Cahyadi, Tedy Agung, Muangthai, Isara, Widodo, Lilik Eko, Ardian, Aldin, Syafrizal, Syafrizal, Rahim, Robbi |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2020, Vol. 2245 Issue 1, p1-10, 10p, 2 Diagrams, 3 Charts, 2 Graphs, 2 Maps |
| Abstrakt: |
The hazard effects do not necessarily differentiate people based on their socioeconomic situation. Individuals or communities who have social vulnerabilities need sufficient resources to deal with disaster events. Data analysis in this study uses computational methods to analyze a set of variables and sample locations with the danger of an eruption of Mount Merapi in Sleman Regency. The purpose of this study is to identify the location of social vulnerability, to understand the importance of the process by which disasters produce impacts on communities in terms of social vulnerability. Self Organizing Map (SOM) as an effective platform for marking social vulnerability per cluster through identifying sites that match their similarity and determining the most relevant variables. The Social Vulnerability Index (SoVI) is a tool used to reconstruct the vulnerability score or index by measuring the level of vulnerability (low to high). To represent the socioeconomic concept, there are 11 vulnerability variables in the data set. These variables for the most part are represent local conditions in the area of studyin Sleman Regency and some are more or less redundant. The size of the SOM map will greatly affect the results of the study, where, the level of accuracy will vary greatly depending on quantification and topographic errors. A very varied level of accuracy comes from exemplification of specimen data in the units of hexagonal figure.It is necessary to remove excessive variables from an accurate SOM analysis of some data samples (outliers). In our preliminary study, the most significant clustering and variables through the SOM approach provides a reliable estimate, while ensuring thenegative and positive values of vulnerability, we use SoVI. As results, unemployment is the most influential variable in all districts, then the people who have middle school education or lower (MIDSCHL), number of migrants (MIGRIN), young age (UNDER14), and number of infants and toddlers (BBTDLR). In efforts to reduce disaster risk through planning and policy action, stakeholders and decision-makers can use both methods to assess social vulnerability. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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