Enhanced Artificial Neural Networks Estimating Water Quality Constraints for the Optimal Water Distribution Systems Design

Andrade, Manuel A.; Choi, Christopher Y.; Lansey, Kevin; Jung, Donghwi

doi:10.1061/(asce)wr.1943-5452.0000663

Cited by 31 publications

(23 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Basic water quality parameters that are used in optimisation models of drinking WDSs are chlorine [27,87,120,143] and chloramine [120], modelled as non-conservative applying first order decay kinetics, adjusted by a higher decay rate in parts of the system when needed [120]. In contrast, conservative water quality parameters are typically important for regional multiquality WDSs.…”

Section: Design For Water Qualitymentioning

confidence: 99%

“…In the former, water quality related expenditures, such as the cost of disinfection [27,120], cost of water treatment [53] or cost of losses incurred by insufficient quality [129], are combined with the system design/capital (and operation) costs into one objective. Alternatively, water quality is included as a constraint to a single-objective model in a form of minimum (and maximum) disinfectant concentrations at the network nodes [87,143]. In the latter, water quality presents a sole objective, which is either water quality benefit (being maximised) [63,131], water quality deficiencies (being minimised) [92,97,144] or water quality reliability (being maximised) [78].…”

Section: Design For Water Qualitymentioning

confidence: 99%

“…Water quality: disinfectant dosage rates (at the sources, at the treatment plants, in the tanks), treatment removal ratios, treatment plant capacities [143] (dosage at the sources), [27] (dosage at the treatment plants), [78] (dosage in the tanks), [53] (removal ratios), [121] (capacities)…”

Section: Decision Variables Reference (An Example)mentioning

confidence: 99%

“…These algorithms were mainly linked with a network simulator EPANET to solve network equations. Because these EPANET simulations, in particular water quality analyses, are very computationally demanding, they were replaced by ANNs [27,87,143] to reduce computational effort.…”

Section: Design For Water Qualitymentioning

confidence: 99%

“…Optimisation methods used to solve WDS design problems including water quality considerations were LP [129], GA [53,87,100,120,143] and differential evolution (DE) [27] for single-objective models, and SMGA [63,131], NSES [92], ε-NSGA-II [97], NSGA-II and SPEA2 integrated with a heuristic Markov-chain hyper-heuristic (MCHH) [144] and ant colony optimisation (ACO) [78] for multi-objective models. These algorithms were mainly linked with a network simulator EPANET to solve network equations.…”

Section: Design For Water Qualitymentioning

confidence: 99%

See 4 more Smart Citations

Lost in Optimisation of Water Distribution Systems? A Literature Review of System Design

Mala-Jetmarova

Sultanova

Savić

2018

Water

130

View full text Add to dashboard Cite

Optimisation of water distribution system design is a well-established research field, which has been extremely productive since the end of the 1980s. Its primary focus is to minimise the cost of a proposed pipe network infrastructure. This paper reviews in a systematic manner articles published over the past three decades, which are relevant to the design of new water distribution systems, and the strengthening, expansion and rehabilitation of existing water distribution systems, inclusive of design timing, parameter uncertainty, water quality, and operational considerations. It identifies trends and limits in the field, and provides future research directions. Exclusively, this review paper also contains comprehensive information from over one hundred and twenty publications in a tabular form, including optimisation model formulations, solution methodologies used, and other important details.

show abstract

Section: Design For Water Qualitymentioning

confidence: 99%

Section: Design For Water Qualitymentioning

confidence: 99%

Section: Decision Variables Reference (An Example)mentioning

confidence: 99%

Section: Design For Water Qualitymentioning

confidence: 99%

Section: Design For Water Qualitymentioning

confidence: 99%

See 3 more Smart Citations

Lost in Optimisation of Water Distribution Systems? A Literature Review of System Design

Mala-Jetmarova

Sultanova

Savić

2018

Water

130

View full text Add to dashboard Cite

show abstract

A reduced complexity model with graph partitioning for rapid hydraulic assessment of sewer networks

Dobson

Watson‐Hill

Muhandes

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

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

Garzón

Kapelan

Langeveld

et al. 2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

Enhanced Artificial Neural Networks Estimating Water Quality Constraints for the Optimal Water Distribution Systems Design

Cited by 31 publications

References 32 publications

Lost in Optimisation of Water Distribution Systems? A Literature Review of System Design

Lost in Optimisation of Water Distribution Systems? A Literature Review of System Design

A reduced complexity model with graph partitioning for rapid hydraulic assessment of sewer networks

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

Contact Info

Product

Resources

About