Deep Learning for Modelling of Urban Drainage Networks: A Physics-informed Surrogate Model Using Measured and Simulated Data
(2022) IWA World Water Congress and Exhibition, 2022- Abstract
- City-wide climate adaptation for pluvial flood mitigation requires fast and reliable simulation tools. Considering the limitations of hydrodynamic models at city-scale simulations, data driven models have high potential in the development of surrogate tools. This study explores the Google DeepMind WaveNet™ model architecture to map hydrological response of catchments onto hydraulic parameters of the pipe network in a physically informed approach to deep learning. The WaveNet-based surrogate model successfully predicted hydraulic head and pipe flow in the network at average Normalized Nash-Sutcliffe Model Efficiency Indices of above 0.8, while boosting simulation speed by a factor of 1000. The developed AI model can be used for different... (More)
- City-wide climate adaptation for pluvial flood mitigation requires fast and reliable simulation tools. Considering the limitations of hydrodynamic models at city-scale simulations, data driven models have high potential in the development of surrogate tools. This study explores the Google DeepMind WaveNet™ model architecture to map hydrological response of catchments onto hydraulic parameters of the pipe network in a physically informed approach to deep learning. The WaveNet-based surrogate model successfully predicted hydraulic head and pipe flow in the network at average Normalized Nash-Sutcliffe Model Efficiency Indices of above 0.8, while boosting simulation speed by a factor of 1000. The developed AI model can be used for different assessment and optimization studies on the drainage network, thanks to its physics-informed structure. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/0b4582bb-f644-45bc-8883-8cca1878deed
- author
- Haghighatafshar, Salar LU ; Bergman, Alexander and Yamanee-Nolin, Mikael LU
- organization
- publishing date
- 2022
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Artificial Intelligence, Machine Learning, Urban Drainage, surrogate models
- host publication
- Proceedings of IWA World Water Congress & Exhibition, Copenhagen 2022
- publisher
- IWA Publishing
- conference name
- IWA World Water Congress and Exhibition, 2022
- conference location
- Copenhagen, Denmark
- conference dates
- 2022-09-11 - 2022-09-15
- project
- Towards intelligent infrastructure planning: Can Machine Learning be used to optimize blue-green stormwater infrastructure?
- language
- English
- LU publication?
- yes
- id
- 0b4582bb-f644-45bc-8883-8cca1878deed
- date added to LUP
- 2022-09-16 10:05:21
- date last changed
- 2023-05-04 15:40:52
@inproceedings{0b4582bb-f644-45bc-8883-8cca1878deed, abstract = {{City-wide climate adaptation for pluvial flood mitigation requires fast and reliable simulation tools. Considering the limitations of hydrodynamic models at city-scale simulations, data driven models have high potential in the development of surrogate tools. This study explores the Google DeepMind WaveNet™ model architecture to map hydrological response of catchments onto hydraulic parameters of the pipe network in a physically informed approach to deep learning. The WaveNet-based surrogate model successfully predicted hydraulic head and pipe flow in the network at average Normalized Nash-Sutcliffe Model Efficiency Indices of above 0.8, while boosting simulation speed by a factor of 1000. The developed AI model can be used for different assessment and optimization studies on the drainage network, thanks to its physics-informed structure.}}, author = {{Haghighatafshar, Salar and Bergman, Alexander and Yamanee-Nolin, Mikael}}, booktitle = {{Proceedings of IWA World Water Congress & Exhibition, Copenhagen 2022}}, keywords = {{Artificial Intelligence; Machine Learning; Urban Drainage; surrogate models}}, language = {{eng}}, publisher = {{IWA Publishing}}, title = {{Deep Learning for Modelling of Urban Drainage Networks: A Physics-informed Surrogate Model Using Measured and Simulated Data}}, year = {{2022}}, }