Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions
In: Water Resources Research, Jg. 58 (2022-05-01), Heft 5
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Zugriff:
Surrogate models replace computationally expensive simulations of physically‐based models to obtain accurate results at a fraction of the time. These surrogate models, also known as metamodels, have been employed for analysis, control, and optimization of water distribution and urban drainage systems. With the advent of machine learning (ML), water engineers have increasingly resorted to these data‐driven techniques to develop metamodels of urban water networks (UWNs). In this article, we review 31 recent articles on ML‐based metamodeling of UWNs to outline the state‐of‐the‐art of the field, identify outstanding gaps, and propose future research directions. For each article, we critically examined the purpose of the metamodel, the metamodel characteristics, and the applied case study. The review shows that current metamodels suffer several drawbacks, including (a) the curse of dimensionality, hindering implementation for large case studies; (b) black‐box deterministic nature, limiting explainability and applicability; and (c) rigid architecture, preventing generalization across multiple case studies. We argue that researchers should tackle these issues by resorting to recent advancements in ML concerning inductive biases, robustness, and transferability. Recently developed neural network architectures, which extend deep learning methods to graph data structures, are preferred candidates for advancing surrogate modeling in UWNs. Furthermore, we foresee increasing efforts for complex applications where metamodels may play a fundamental role, such as uncertainty analysis and multi‐objective optimization. Lastly, the development and comparison of ML‐based metamodels can benefit from the availability of new benchmark datasets for urban drainage systems and realistic complex networks. Analysis and improvement of urban water networks requires hydrodynamic models. Since these models are computationally expensive, researchers and engineers often resort to fast alternatives known as surrogate models. With the rise of artificial intelligence, machine learning methods have been increasingly used for surrogate modeling of urban water networks. In this study, we thoroughly reviewed recent articles in the field to outline the current state‐of‐the‐art and propose future research directions. While many successful applications already exist, we found that these models have three main limiting factors: (a) they need large amounts of data, (b) they are not explainable, and (c) they are too specific to each case. We argue that researchers can overcome these limitations by considering recent advancements in artificial intelligence and implement modeling techniques that better leverage the structure of the underlying data. Other promising directions include developing comprehensive benchmark databases and leveraging surrogate models for more complex applications. Machine learning surrogate models have been widely employed for a variety of applications concerning urban water networksNew research should investigate machine learning metamodels that account for inductive biases, robustness, and transferabilityFurther research should focus on complex problems involving uncertainty and multi‐objective optimization, as well as improved benchmarking Machine learning surrogate models have been widely employed for a variety of applications concerning urban water networks New research should investigate machine learning metamodels that account for inductive biases, robustness, and transferability Further research should focus on complex problems involving uncertainty and multi‐objective optimization, as well as improved benchmarking
Titel: |
Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions
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Autor/in / Beteiligte Person: | Garzón, A. ; Kapelan, Z. ; Langeveld, J. ; Taormina, R. |
Link: | |
Zeitschrift: | Water Resources Research, Jg. 58 (2022-05-01), Heft 5 |
Veröffentlichung: | 2022 |
Medientyp: | serialPeriodical |
ISSN: | 0043-1397 (print) |
DOI: | 10.1029/2021WR031808 |
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