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A metamodel for disaster risk models
Nur W.H.a,b, Azizah F.N.a, Akbar S.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
b Research Center for Geotechnology, Indonesian Institute of Science, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2015 IEEE.As the world faces an increasing number of both natural and social disasters, attempts to support disaster risk reduction are also increasing. Although there is a general rule to calculate the disaster risk on an area based on the components of hazard, vulnerability, and capacity, disaster risk studies result in a number of disaster risk models which present different characteristics in terms of the number of components involved, indicators, and the calculations. This poses a difficulty for disaster analysts to choose the most appropriate model to calculate the disaster risk of an area. Moreover, they often need to adapt the existing models or even to create new models in order to provide the most suitable way of calculating the disaster risks. Therefore, a mechanism that enables the use of different kinds of disaster risk model and the creation of new models is required. This paper presents a metamodel of disaster risk based on a study on a number of disaster risk models used in Indonesia: The BNPB (Badan Nasional Penanggulangan Bencana) disaster risk model, the volcanic disaster risk model using SMART (Simple Multi Attribute Rating Technique) method, and the tidal flooding disaster risk model using fuzzy method. The metamodel is presented in an entity-relationship model. It is basically a spatial data model since the components and indicators for the calculation of disaster risks are always associated to the space on earth. The metamodel is implemented on top of ArcGIS software. Using Phyton Add-in, the software is adapted by adding new functionalities to calculate the disaster risk of an area and to create new disaster risk models.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]ArcGIS,BNPB,Disaster risk reductions,Meta model,Risk maps,Risk model[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]ArcGIS,BNPB,disaster,disaster risk map,disaster risk model,disaster risk reduction,metamodel[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICODSE.2015.7436964[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]