Quantitative Multi-risk Modelling and Management Using Bayesian Networks

Abstract

In many regions of the world, several natural hazards may act within the same time frame. Neglecting or underestimating interactions among different hazards may lead to underestimation of the overall risk and further to poor risk management. Implementation of effective and efficient risk management strategies requires that all relevant threats are assessed and considered. However, due to the differing characteristics of hazards, few quantitative models exist that can perform all the computations required for a complete multi-risk assessment. In this paper, a quantitative multi-risk management model using Bayesian networks (BaNMuR) is proposed, which could account for the assessment of cascading hazards, time-dependent vulnerability estimation and selection of optimal risk management strategies. The model was developed as part of the EU FP7 Collaborative Research Project MATRIX. An application example of the BaNMuR model for assessing the risk of tsunami triggered by rockslide is presented in the paper. The proposed multi-risk modelling evaluates the effect of interaction between single risks quantitatively, provides a more rational estimate of multiple risks and helps the decision-makers choose the best risk reduction strategy.