[HTML][HTML] The role of deep learning in urban water management: A critical review

G Fu, Y Jin, S Sun, Z Yuan, D Butler - Water Research, 2022 - Elsevier
Deep learning techniques and algorithms are emerging as a disruptive technology with the
potential to transform global economies, environments and societies. They have been …

A review of graph and complex network theory in water distribution networks: Mathematical foundation, application and prospects

X Yu, Y Wu, F Meng, X Zhou, S Liu, Y Huang, X Wu - Water Research, 2024 - Elsevier
Graph theory (GT) and complex network theory play an increasingly important role in the
design, operation, and management of water distribution networks (WDNs) and these tasks …

Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks

Z Zhang, W Tian, C Lu, Z Liao, Z Yuan - Water Research, 2024 - Elsevier
Physics-based models are computationally time-consuming and infeasible for real-time
scenarios of urban drainage networks, and a surrogate model is needed to accelerate the …

Graph neural networks for pressure estimation in water distribution systems

H Truong, A Tello, A Lazovik… - Water Resources …, 2024 - Wiley Online Library
Pressure and flow estimation in water distribution networks (WDNs) allows water
management companies to optimize their control operations. For many years, mathematical …

A spatiotemporal deep learning approach for urban pluvial flood forecasting with multi-source data

B Burrichter, J Hofmann, J Koltermann da Silva… - Water, 2023 - mdpi.com
This study presents a deep-learning-based forecast model for spatial and temporal
prediction of pluvial flooding. The developed model can produce the flooding situation for …

Bridging hydraulics and graph signal processing: A new perspective to estimate water distribution network pressures

X Zhou, S Liu, W Xu, K Xin, Y Wu, F Meng - Water Research, 2022 - Elsevier
The low spatial density of monitored nodal pressures (nodal heads) has already become a
bottleneck restricting the development of smart technologies for water distribution networks …

Graph-based learning for leak detection and localisation in water distribution networks

GÖ Garðarsson, F Boem, L Toni - IFAC-PapersOnLine, 2022 - Elsevier
We propose the application of geometric deep learning techniques to the challenging leak
detection and isolation problem in water distribution networks (WDNs). Specifically, we train …

A convenient and stable graph-based pressure estimation methodology for water distribution networks: Development and field validation

X Zhou, J Zhang, S Guo, S Liu, K Xin - Water Research, 2023 - Elsevier
Accurate estimation of unknown nodal pressures (nodal heads) is necessary for efficient
operation and management of water distribution networks (WDNs), but existing methods …

Wasserstein-Enabled Leaks Localization in Water Distribution Networks

A Ponti, I Giordani, A Candelieri, F Archetti - Water, 2024 - mdpi.com
Leaks in water distribution networks are estimated to account for up to 30% of the total
distributed water; moreover, the increasing demand and the skyrocketing energy cost have …

Leveraging deep reinforcement learning for water distribution systems with large action spaces and uncertainties: DRL-EPANET for pressure control

A Belfadil, D Modesto, J Meseguer… - Journal of Water …, 2024 - ascelibrary.org
Deep reinforcement learning (DRL) has undergone a revolution in recent years, enabling
researchers to tackle a variety of previously inaccessible sequential decision problems …