Estimating uncertainty in flood model outputs using machine learning informed by Monte Carlo analysis

Numerous studies have extensively employed Monte Carlo-based methods to characterize uncertainty in flood modelling due to their simplicity and flexibility. However, these approaches are time-consuming and computationally extensive. Recognizing the need for efficiency, machine learning has emerged as a promising alternative with comparable results. This paper introduces an advanced uncertainty estimation method for flood modelling, utilizing the Bayes Neural Network with Bayes by Backprop. The model aims to forecast variations in flood modelling predictions caused by uncertainty in the Digital Elevation Model generation process, offering a more efficient and accurate approach to address the challenges posed by traditional Monte Carlo methods.