Uncertainty quantification for disaster modelling: flooding as a case study

Disaster's behaviour recognition has become an area of interest for researchers in the last decades especially with climate changes that have contributed in disaster's severity which made their prediction more complicated. The aim of this work is to use uncertainty quantification tools to describe the flooding behaviour in Morocco based on deaths numbers using the Interpolation-based Approximations (Collocation) method in Hilbert Space in order to find the Cumulative Distribution Function (CDF) and using derivation by Dirac's approximations to determine the Probability Density Function (PDF), with the aim of describing the mortality of the disaster over regions and finally detecting the areas more sensitive to this disaster. The use of uncertainty quantification models for flooding goes beyond the ordinary application for data analysis, but it constitutes a decision-making tool for governments and organizations in the field of disaster management especially for disaster prediction.