Dataset and algorithms for the Bayesian framework to assess and create risk maps of groundwater flooding

This repository is part of the research "A Bayesian Framework to Assess and Create Maps of Groundwater Flooding", written by Pablo Merchán-Rivera, Alexandra Geist, Markus Disse, Jingshui Huang, and Gabriele Chiogna. The algorithms and scripts apply the framework to create maps of groundwater flooding susceptibility in a numerical model that simulates the groundwater flood event. The algorithm includes the application of the elementary effects method, a Markov Chain Monte Carlo inference using the DREAM algorithm, and the exploration of the predictive posterior distributions of the groundwater heads. The simulation performed in the research can be run using this repository to reproduce the research results.