Development of a matrix based statistical framework to compute weight for composite hazards, vulnerability and risk assessments

Abstract

This paper introduces a new method to compute weights of indicators in climate modelling for composite hazard, vulnerability, and risk assessment. To develop this ’matrix based statistical framework’ method (MSF), valuation of correlation matrix and Eigenvector associated with Eigenvalue is considered from Pearson correlation coefficients. Findings show that the vulnerability map prepared by using MSF with 15 socio-economic indicators has the maximum similarity (49%) with the prototype compared to other weight methods. Selection of relative weights for different indicators in climate modelling is a critical step during assessment of composite hazards.

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Keywords

COMPUTATION, MATHEMATICAL MODELS, SIMULATION, CLIMATE DATA, CLIMATE MODELLING, RISK ASSESSMENT, INDICATORS, CLIMATE CHANGE VULNERABILITY, BANGLADESH, SOUTH ASIA

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