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Deep Learning for Modelling of Urban Drainage Networks: A Physics-informed Surrogate Model Using Measured and Simulated Data

Haghighatafshar, Salar LU orcid ; Bergman, Alexander and Yamanee-Nolin, Mikael LU (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:
author
; and
organization
publishing date
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}},
}