Estimating uncertainty in flood model outputs using machine learning informed by Monte Carlo analysis - Script and Data 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 five big folders/files and two Github files:

• Model1_classification_proportion folder: 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 corresponding 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 folder: 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 corresponding to 5-, 10-, 20-m resolutions under 7zip format. Please unzip them to see the previous results and to use them. Another file is "Analysis" for formatting the results after predictions.
• Model3_regression_sd folder: 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. Another file is "Analysis" for formatting the results after predictions.
• Comparisons_model1_model2.7z: Stores all files used to generate confusion matrices, rmse, and accuracy metrics.
• Figures_Tables.7zip: Stores all figures an tables used in the publication.
• Github_FloodUnEn_package.zip: Stores Github source for package named "FloodUnEs" to predict uncertainty using BNNBB models. Please also see here for the online version
• Github_MC_simulation_generation.zip: Stores Github source for generating Monte Carlo simulations. Please also see here for the online version. For the whole dataset that were already generated within this Monte Carlo framework, because it is too large to be uploaded (about 4TB), please contact the author via tmn52@uclive.ac.nz, if necessary.