Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning
Autor: | Rocco Palmitessa, Morten Grum, Allan Peter Engsig-Karup, Roland Löwe |
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Jazyk: | angličtina |
Rok vydání: | 2022 |
Předmět: |
FOS: Computer and information sciences
Scientific machine learning Computer Science - Machine Learning Environmental Engineering Hydrodynamic simulation Ecological Modeling Physics Water Pollution Surrogate model Machine Learning (cs.LG) Urban drainage Computational Engineering Finance and Science (cs.CE) Machine Learning Hydrodynamics Hydrology Computer Science - Computational Engineering Finance and Science Waste Management and Disposal Water Science and Technology Civil and Structural Engineering |
Zdroj: | Palmitessa, R, Grum, M, Engsig-Karup, A P & Löwe, R 2022, ' Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning ', Water Research, vol. 223, 118972 . https://doi.org/10.1016/j.watres.2022.118972 |
Popis: | We propose and demonstrate a new approach for fast and accurate surrogate modelling of urban drainage system hydraulics based on physics-guided machine learning. The surrogates are trained against a limited set of simulation results from a hydrodynamic (HiFi) model. Our approach reduces simulation times by one to two orders of magnitude compared to a HiFi model. It is thus slower than e.g. conceptual hydrological models, but it enables simulations of water levels, flows and surcharges in all nodes and links of a drainage network and thus largely preserves the level of detail provided by HiFi models. Comparing time series simulated by the surrogate and the HiFi model, R2 values in the order of 0.9 are achieved. Surrogate training times are currently in the order of one hour. However, they can likely be reduced through the application of transfer learning and graph neural networks. Our surrogate approach will be useful for interactive workshops in initial design phases of urban drainage systems, as well as for real time applications. In addition, our model formulation is generic and future research should investigate its application for simulating other water systems. |
Databáze: | OpenAIRE |
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