No abstract

When planning flood protection, agencies are confronted with uncertainty in the design flood magnitude. In particular, the required capacity may increase in the future and render the protection insufficient. This problem can be adressed by applying a safety factor to the design capacity. We propose a Bayesian quantitative sequential decision model that identifies a cost-optimal safety factor in the face of uncertainty. It takes into account the flexibility of the protection system, that is, how costly it is to adjust. We focus on the description of the decision model and on the concept of flexibility, investigating only the effect of uncertainty from the historic flood record. Extension to other types of uncertainty is possible. The model is implemented for a catchment in Germany. Various degrees of uncertainty are investigated by using different lengths of historic records. The optimal safety factor decreases with decreasing uncertainty and with increasing system flexibility. K E Y W O R D SBayesian decision analysis, decision support under uncertainty, flexible protection strategies, flood protection, natural hazards, safety factor, uncertainty quantification

A probabilistic model for estimating tunnel excavation time is learnt with data from past tunnel projects. The model is based on the Dynamic Bayesian Network technique. The model inputs are determined through an analysis of data from three tunnels built by means of the conventional tunneling method. The data motivate the development of a novel probability distribution to describe the excavation performance. In addition, the probability of construction failure events and the delay caused by such failures are estimated using databases available in the literature. The model is applied to a case study, in which it is demonstrated how observations from the tunnel construction process can be included to continuously update the prediction of excavation time.

Cost-benefit analysis (CBA) is commonly applied as a tool for deciding on risk protection. With CBA, one can identify risk mitigation strategies that lead to an optimal tradeoff between the costs of the mitigation measures and the achieved risk reduction. In practical applications of CBA, the strategies are typically evaluated through efficiency indicators such as the benefit-cost ratio (BCR) and the marginal cost (MC) criterion. In many of these applications, the BCR is not consistently defined, which, as we demonstrate in this article, can lead to the identification of suboptimal solutions. This is of particular relevance when the overall budget for risk reduction measures is limited and an optimal allocation of resources among different subsystems is necessary. We show that this problem can be formulated as a hierarchical decision problem, where the general rules and decisions on the available budget are made at a central level (e.g., central government agency, top management), whereas the decisions on the specific measures are made at the subsystem level (e.g., local communities, company division). It is shown that the MC criterion provides optimal solutions in such hierarchical optimization. Since most practical applications only include a discrete set of possible risk protection measures, the MC criterion is extended to this situation. The findings are illustrated through a hypothetical numerical example. This study was prepared as part of our work on the optimal management of natural hazard risks, but its conclusions also apply to other fields of risk management.