Source Codes and Test Datasets for "Data-Driven Pluvial Flood Modelling for Urban Regions: An Investigation of Spatial Generalizability"

This repository contains the source codes, trained models, and test datasets used in the study “Data-Driven Pluvial Flood Modelling for Urban Regions: An Investigation of Spatial Generalizability”, submitted to Environmental Modelling & Software.

The materials are organized as follows:

• scripts/ Python scripts used to develop and train the three machine learning and deep learning models (Random Forest, XGBoost, and U-Net) for each scenario.

• models/ Trained model files corresponding to each method and scenario.

• input data/ Minimal test datasets required for model performance assessment and replication of the reported results.

• Performance evaluation of all models.py Unified script to reproduce model performance metrics (e.g., RMSE, MAE) and visual comparisons using the provided test datasets.

Each folder includes four subdirectories corresponding to the simulation cases:

• depth – with

• depth– without

• velocity – with

• velocity – without

Within each subdirectory, models, data, and scripts for the three predictive methods (RF, XGBoost, and U-Net) are provided.

All scripts were developed in Python 3.10.13 using:

• scikit-learn 1.5.1 for Random Forest

• xgboost 2.1.4 for XGBoost

• tensorflow 2.10.0 for U-Net