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

Necessary data here are used for training and testing the Bayes Neural Network with Bayes by Backprop models - model 1, 2, and 3. There are three big folders:<br>Model1_classification_proportion: Model 1 is used to predict the labelled map identifying where is always, sometimes, and never flooded. This output will be used as an input for the model 2. There are three 3 files correspond to 5-, 10-, and 20-m resolutions under 7zip format. Please unzip them to see the previous results and to use them.Model2_regression_proportion: Model 2 is used to predict the propotion of each pixel being flooded (the pF map) using the output of the model 1 as one of the inputs. There are three 3 files correspond to 5-, 10-, 20-m resolutions under 7zip format. Please unzip them to see the previous results and to use them.Model3_regression_sd: Model 3 is used to predict the standard deviation of maximum flood depth (the sdMWD map). There are three 3 files correspond to 5-, 10-, and 20-m resolutions under 7zip format. Please unzip them to see the previous resutls and to use them.Please also refer to here to see the scripts for generating the Monte Carlo uncertainty data for developing the model 1,2, and 3, and here to see the scripts/package to predict the pF and sdMWD maps. For the necessary data used to generate the Monte Carlo uncertainty data, it is too large to be uploaded (about 4TB), so please contact the author via [e-mail].