Machine learning‐based surrogate modeling for urban water networks: review and future research directions
A Garzón, Z Kapelan, J Langeveld… - Water Resources …, 2022 - Wiley Online Library
Surrogate models replace computationally expensive simulations of physically‐based
models to obtain accurate results at a fraction of the time. These surrogate models, also …
models to obtain accurate results at a fraction of the time. These surrogate models, also …
Real-time water quality prediction in water distribution networks using graph neural networks with sparse monitoring data
Z Li, H Liu, C Zhang, G Fu - Water Research, 2024 - Elsevier
Ensuring the safety and reliability of drinking water supply requires accurate prediction of
water quality in water distribution networks (WDNs). However, existing hydraulic model …
water quality in water distribution networks (WDNs). However, existing hydraulic model …
Physics-informed neural networks for hydraulic transient analysis in pipeline systems
In water pipeline systems, monitoring and predicting hydraulic transient events are important
to ensure the proper operation of pressure control devices (eg, pressure reducing valves) …
to ensure the proper operation of pressure control devices (eg, pressure reducing valves) …
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 …
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 …
Deep fuzzy mapping nonparametric model for real-time demand estimation in water distribution systems: A new perspective
Hydraulic modeling has been recognized as a valuable tool for improving the design,
operation, and management of water distribution systems (WDSs) as it allows engineers to …
operation, and management of water distribution systems (WDSs) as it allows engineers to …
Reconstructing unsaturated infiltration behavior with sparse data via physics-informed deep learning
P Lan, J Su, S Zhu, J Huang, S Zhang - Computers and Geotechnics, 2024 - Elsevier
In this paper, we propose a novel framework, physics-informed deep learning (PIDL), which
combines a set of data-and physics-driven modeling methods along with an uncertainty …
combines a set of data-and physics-driven modeling methods along with an uncertainty …
Bridging technology transfer boundaries: Integrated cloud services deliver results of nonlinear process models as surrogate model ensembles
Environmental models are often essential to implement projects in planning, consulting and
regulatory institutions. Research models are often poorly suited to such applications due to …
regulatory institutions. Research models are often poorly suited to such applications due to …
[HTML][HTML] Towards transferable metamodels for water distribution systems with edge-based graph neural networks
Data-driven metamodels reproduce the input-output mapping of physics-based models
while significantly reducing simulation times. Such techniques are widely used in the design …
while significantly reducing simulation times. Such techniques are widely used in the design …
Reconstructing nodal pressures in water distribution systems with graph neural networks
Knowing the pressure at all times in each node of a water distribution system (WDS)
facilitates safe and efficient operation. Yet, complete measurement data cannot be collected …
facilitates safe and efficient operation. Yet, complete measurement data cannot be collected …