[HTML][HTML] The role of deep learning in urban water management: A critical review
Deep learning techniques and algorithms are emerging as a disruptive technology with the
potential to transform global economies, environments and societies. They have been …
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
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 …
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
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 …
scenarios of urban drainage networks, and a surrogate model is needed to accelerate the …
Graph neural networks for pressure estimation in water distribution systems
Pressure and flow estimation in water distribution networks (WDNs) allows water
management companies to optimize their control operations. For many years, mathematical …
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 …
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
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 …
bottleneck restricting the development of smart technologies for water distribution networks …
Graph-based learning for leak detection and localisation in water distribution networks
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 …
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
Accurate estimation of unknown nodal pressures (nodal heads) is necessary for efficient
operation and management of water distribution networks (WDNs), but existing methods …
operation and management of water distribution networks (WDNs), but existing methods …
Wasserstein-Enabled Leaks Localization in Water Distribution Networks
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 …
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 …
researchers to tackle a variety of previously inaccessible sequential decision problems …