Data for paper "Evaluation of Graph Neural Networks for Urban Drainage Metamodeling: Key Components and Transferability Analysis"

This dataset supports the article “Evaluation of Graph Neural Networks for Urban Drainage Metamodeling: Key Components and Transferability Analysis” (Garzón et al., 2025). It contains SWMM 5.1.015 simulation data and machine learning artifacts used to develop, train, and evaluate a Graph Neural Network based metamodel for two urban drainage systems: Loenen and Tuindorp. The data was produced as part of a PhD project at TU Delft to assess metamodel accuracy, speed up, data efficiency, and transferability under different conditions.

The folder "data" includes SWMM network files (.inp) and simulation outputs (lateral inflow, water levels, flows, and .out files) for training, validation, and testing. Testing included Dry Weather Flow conditions. Loenen includes complete training, validation, and testing sets. Tuindorp includes previously published training and validation data and newly added DWF testing data. All simulations were run using dynamic wave routing and rainfall events from the original case studies. No personal or sensitive data is included.

The folder "saved_objects" contains the machine learning components used in the study: pre trained model weights in PyTorch, feature normalizers used for consistent preprocessing, and pre processed time window datasets stored as PyTorch Geometric Data objects. These artifacts allow full reproduction of the metamodeling experiments and support transfer learning and further research.

The dataset enables researchers to replicate the methodology used in the paper, which combines SWMM hydrodynamic simulations with graph based deep learning models such as GINE and GAT trained on graph structured time series data. The workflow includes rainfall event simulation, feature normalization, graph construction, supervised learning, and evaluation on unseen inflow conditions including DWF baselines.

This work is supported by the TU Delft AI Labs programme.