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The results explain the prediction of building vulnerability based on the building damaged of the Yogyakarta earthquake 2006.
Additionally, the results explain the estimation or prediction of building vulnerability based on the building damaged of the Yogyakarta earthquake 2006.
Therefore, the main objective of this study has been the provision of a probabilistic model of seismic building vulnerability based on the damage data of the last big earthquake.
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Figure 2b shows the estimated distribution of building vulnerability classes based on ISTAT 1991 data and its updated values to year 2011.
The SCMAI method re-predicts the groundwater vulnerability based on the different AI model prediction values.
We assessed components of vulnerability based on four metrics of current and projected future climate suitability.
The VEP approach defines vulnerability based on household expected consumption expenditure above a given threshold.
The clusters were then stratified into two categories of vulnerability based on the above characteristics.
The objectives of this study is to conduct a building vulnerability assessment based on probabilistic analysis and to estimate the vulnerability of buildings and population in the study area under different scenarios of population distribution.
Initially implemented at a detailed scale, the loss assessment procedure was then developed at a municipal scale through the introduction of several critical assumptions, such as that a set of intensity values rounded off at municipal scale and an updated building vulnerability analysis based on revised ISTAT data processing.
In this paper, we present a framework to automatically predict vulnerabilities based on CCC metrics.
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