GNN-UDS

A GNN-based surrogate model of urban drainage networks.

Please feel free to read or cite our paper below.

Zhang, Z., Tian, W., Lu, C., Liao, Z. and Yuan, Z. 2024. Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks. Water Research, 263, 122142. https://doi.org/10.1016/j.watres.2024.122142

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How-to

1. generate labels

   python main.py --simulate --env (env_name) --data_dir (data_name) (--edge_fusion) (--act)
   

   Simulations are made to generate training data at ./envs/data/env_name/data_name/.

2. training

   python main.py --train --env (env_name) --data_dir (data_name)  --model_dir (model_name) (--edge_fusion) (--act) (--conv GAT) (--recurrent Conv1D) (--batch_size 64) (--epochs 20000) (--if_flood) (--norm) (--resnet) (--seq_in 10) (--seq_out 10)
   

   The model structure is built and trained with data at data_dir for epochs. Details of the model and training parameters refer to config.yaml. The trained model and training loss logging are saved at ./model/env_name/model_name/.

3. testing

   python main.py --test --env (env_name) --model_dir (model_name) --result_dir (result_name) (--edge_fusion) (--act) (--conv GAT) (--recurrent Conv1D) (--if_flood) (--norm) (--resnet) (--seq_in 10) (--seq_out 10)
   

   The model is loaded to emulate the drainage network in various rainfalls. Details of the model and testing parameters refer to config.yaml and parser func at main.py. The testing states, performance (perfs), settings and prediction results of each rainfall are saved at ./result/env_name/result_name/.

Drainage networks

1. astlingen

   • Combined sewer network
   • 30 nodes (23 junctions, 6 tanks and 1 outfall)
   • 29 edges (23 conduits and 6 outflow orifices)
   • 10-yr rainfall monitoring data of 4 gauges are included
   • Details refer to SWMM-Astlingen.

2. shunqing

   • Stormwater network
   • 113 nodes (105 junctions and 8 outfalls)
   • 131 conduits and 106 subcatchments (cover 33.02 km2)
   • 148 synthetic rainfalls included with duration of 6-24 hrs
   • Details refer to ga_ann_for_uds.

3. RedChicoSur

   • Stormwater network
   • 443 nodes (442 junctions and 1 outfall)
   • 444 edges (390 conduits and 54 orifices)
   • 2-hr synthetic rainfalls (Chicago hytograph) included
   • Details refer to MatSWMM.

Requirements

• tensorflow == 2.6.0
• keras == 2.6.0
• tensorflow_probability == 0.11.0
• spektral == 1.2.0
• protobuf == 3.20.0
• pyswmm == 1.5.1
• pystorms == 1.0.0
• swmm-api == 0.2.0.18.3
• matplotlib == 3.5.2
• pymoo == 0.6.0