A Bayesian network approach to coastal storm impact modeling

In this paper we develop a Bayesian network (BN) that relates offshore storm conditions to their accompanying flood characteristics and damages to residential buildings, following on the trend of integrated flood impact modeling. It is based on data from hydrodynamic storm simulations, information on land use and a depth-damage curve. The approach can easily be applied to any site. We have chosen the Belgian village Zeebrugge as a case study, although we use a simplified storm climate. The BN can predict spatially varying inundation depths and building damages for specific storm scenarios and diagnose under which storm conditions and where on the site the highest impacts occur.Non UBCUnreviewedThis collection contains the proceedings of ICASP12, the 12th International Conference on Applications of Statistics and Probability in Civil Engineering held in Vancouver, Canada on July 12-15, 2015. Abstracts were peer-reviewed and authors of accepted abstracts were invited to submit full papers. Also full papers were peer reviewed. The editor for this collection is Professor Terje Haukaas, Department of Civil Engineering, UBC Vancouver.FacultyOthe