DOI: 10.26188/24312658
title: Surrogate flood model comparison - Datasets and python code

description: Data used for publication in <b>"Assessment of surrogate models for flood inundation: The physics-guided LSG model vs. state-of-the-art machine learning models"</b>. Five surrogate models for flood inundation is to emulate the results of high-resolution hydrodynamic models. The surrogate models are compared based on accuracy and computational speed for three distinct case studies namely Carlisle (United Kingdom), Chowilla floodplain (Australia), and Burnett River (Australia).The dataset is structured in 5 files - "Carlisle", "Chowilla", "BurnettRV", "Comparison_results", and "Python_data". As a minimum to run the models the "Python_data" file and one of "Carlisle", "Chowilla", or "BurnettRV" are needed. We suggest to use the "Carlisle" case study for initial testing given its small size and small data requirement."Carlisle", "Chowilla", and "BurnettRV" files These files contain hydrodynamic modelling data for training and validation for each individual case study, as well as specific Python scripts for training and running the surrogate models in each case study. There are only small differences between each folder, depending on the hydrodynamic model trying to emulate and input boundary conditions (input features).Each case study file has the following folders:<i>Geometry_data</i>: DEM files, .npz files containing of the high-fidelity models grid (XYZ-coordinates) and areas (Same data is available for the low-fidelity model used in the LSG model), .shp files indicating location of boundaries and main flow paths (mainly used in the LSTM-SRR model). <i>XXX_modeldata</i>: Folder to storage trained model data for each XXX surrogate model. For example, GP_EOF_modeldata contains files used to store the trainined GP-EOF model.<i>HD_model_data</i>: High-fidelity (And low-fidelity) simulation results for all flood events of that case study. This folder also contains all boundary input conditions.<i>HF_EOF_analysis</i>: Storing of data used in the EOF analysis. EOF analysis is applied for the LSG, GP-EOF, and LSTM-EOF surrogate models. <i>Results_data</i>: Storing results of running the evaluation of the surrogate models.<i>Train_test_split_data</i>: The train-test-validation data split is the same for all surrogate models. The specific split for each cross-validation fold is stored in this folder.And Python files:<i>YYY_event_summary</i>, <i>YYY_Extrap_event_summary</i>: Files containing overview of all events, and which events are connected between the low- and high-fidelity models for each YYY case study.<i>EOF_analysis_HFdata_preprocessing</i>, <i>EOF_analysis_HFdata</i>: Preprocessing before EOF analysis and the EOF analysis of the high-fidelity data. This is used for the LSG, GP-EOF, and LSTM-EOF surrogate models.<i>Evaluation</i>, <i>Evaluation_extrap</i>: Scripts for evaluating the surrogate model for that case study and saving the results for each cross-validation fold.<i>train_test_split</i>: Script for splitting the flood datasets for each cross-validation fold, so all surrogate models train on the same data.<i>XXX_training</i>: Script for training each XXX surrogate model.<i>XXX_preprocessing</i>: Some surrogate models might rely on some information that needs to be generated before training. This is performed using these scripts."Comparison_results" fileFiles used for comparing surrogate models and generate the figures in the paper <b>"Assessment of surrogate models for flood inundation: The physics-guided LSG model vs. state-of-the-art machine learning models"</b>. Figures are also included. "Python_data" fileFolder containing Python script with utility functions for setting up, training, and running the surrogate models, as well as for evaluating the surrogate models. This folder also contains a python_environment.yml file with all Python package versions and dependencies.This folder also contains two sub-folders:<i>LSG_mods_and_func</i>: Python scripts for using the LSG model. Some of these scripts are also utilized when working with the other surrogate models. <i>SRR_method_master_Zhou2021</i>: Scripts obtained from https://github.com/yuerongz/SRR-method. Small edits have for speed and use in this study.