Optimal architecture for artificial neural networks as pressure estimator

Souza, Rui Gabriel; Brentan, Bruno Melo; Lima, Gustavo Meirelles

doi:10.1590/2318-0331.262120210100

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Also, it is not possible to know the number of layers and units that yield the best performance. For example, Modesto De Souza et al (2021) tested multiple architectures of an MLP for pressure estimation in a WDS. Their results suggest that the optimal number of layers is two but this can vary for other applications.…”

Section: Metamodeling Methodsmentioning

confidence: 99%

Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions

Garzón

Kapelan

Langeveld

et al. 2022

Water Resources Research

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Urban water networks (UWNs) comprise drinking water distribution and urban drainage systems (WDS and UDS). The former are responsible for supplying drinking water to cities and the latter for evacuating wastewater and stormwater runoff. These infrastructures are a fundamental part of the city and are directly linked to its development (Brown et al., 2009). Each of these systems faces challenges to improve and maintain quality service in a dynamic urban environment under a widening range of climatic conditions; especially, in a climate-changing situation. Designing, optimizing, and intervening in these systems requires approximating their hydraulic behavior. Many models have been developed in the past years for simulating UWNs, for example, SOBEK (Deltares, n.d.), WaterCAD (Bentley®, n.d.), EPA-SWMM (Rossman, 2010), among others. Traditional modeling approaches are either based on accurate description of the physical processes (e.g., EPANET;Rossman et al., 2000) or rely on simplified conceptual models (e.g., SIMPOL;Dempsey et al., 1997); nonetheless, the former usually entail computationally expensive calculations while the latter lack fidelity, that is, level of detail. Applications such as optimization, real-time modeling, and uncertainty analysis need efficient models for evaluating the performance

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Section: Metamodeling Methodsmentioning

confidence: 99%

Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions

Garzón

Kapelan

Langeveld

et al. 2022

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…In WDSs, there are two cases of variations on the number of layers: Sayers et al (2019) Also, it is not possible to know the number of layers and units that yield the best performance. For example, Modesto De Souza et al, (2021) tested multiple architectures of an MLP for pressure estimation in a WDS. Their results suggest that the optimal number of layers is two but this can vary for other applications.…”

Section: Metamodelling Methodsmentioning

confidence: 99%

Machine learning-based surrogate modelling for Urban Water Networks: Review and future research directions

Garzón

Kapelan

Langeveld

et al. 2022

Preprint

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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 optimisation 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. In this manuscript, we review 31 recent papers on ML-based metamodeling of urban water networks to outline the state-of-the-art of the field, identify outstanding gaps, and propose future research directions. For each paper, 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 i) the curse of dimensionality, hindering implementation for large case studies; ii) black-box deterministic nature, limiting explainability and applicability; and iii) 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. The recently developed Graph Neural Network architecture, which extends deep learning methods to graph data structures, is a preferred candidate for advancing surrogate modelling in urban water networks. Furthermore, we foresee increasing efforts for complex applications where metamodels may play a fundamental role, such as uncertainty analysis and multi-objective optimisation. Lastly, the development and comparison of ML-based metamodel can benefit from the availability of new benchmark datasets for urban drainage systems and realistic complex networks.

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Optimal architecture for artificial neural networks as pressure estimator

Cited by 2 publications

References 16 publications

Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions

Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions

Machine learning-based surrogate modelling for Urban Water Networks: Review and future research directions

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