Abstract
Selection of relative weights for different indicators is a critical step during assessment of composite hazards, vulnerability, and risk. While assigning weight to an indicator, it is important to consider the influence of an individual indicator on a particular composite index. In general, the larger the weight of the indicator, the higher the importance of that indicator compared to other indicators. In this study, a new matrix based statistical framework (MSF) for weight assignment is developed that can be considered as the simplest and most accurate method for assigning weights for a large number of indicators. This method (MSF) is based on the valuation of the correlation matrix and Eigenvector associated with Eigenvalue. Relying on the inter build up methodology, MSF can fulfill some built-in gaps among other weightage methods. It can also directly give the ‘decision’ to select the relative weights that are found from the Eigenvector corresponding to the largest Eigenvalue. The new method is applied by assigning weights to 15 socio-economic indicators and assessed vulnerability and risk in the Bangladesh coast. While comparing with other weight methods, it is found that MSF gives the most acceptable physical explanation about the relative values of weights of indicators. In terms of accuracy, MSF is found to be most accurate compared to other weight methods. When large numbers of indicators are involved in an application, MSF is found to be relatively simple and easy to apply compared to other methods.
Highlights
Introduction and Statement of ProblemResponding to climate change is widely acknowledged as one of the greatest challenges facing society [1,2,3]
This paper introduces a new method to compute weights of indicators for composite hazard, vulnerability, and risk assessments
To demonstrate the application of matrix based statistical framework’ (MSF), vulnerability, and risk along the Bangladesh coast is assessed by using MSF as the weighting method
Summary
Introduction and Statement of ProblemResponding to climate change is widely acknowledged as one of the greatest challenges facing society [1,2,3]. Every day new issues related to socio-economic factors and ongoing climate change impacts are emerging. To address this problem and related concerns, hazard, vulnerability, and risk assessments are required that can help to understand the complex set of factors that contribute to the assessment of how communities will adapt to changing environmental conditions. Integrated assessments focusing on climatic hazard specific risk minimization on coastal deltas are rare [4,5,6,7]. Vulnerability and risk in coastal deltas are not fully understood and the identification of risk reduction and adaptation strategies following appropriate ranking of indicators are often based on incomplete assumptions.
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