A Reduced Complexity Model With Graph Partitioning for Rapid Hydraulic Assessment of Sewer Networks

Dobson, Barnaby; Watson‐Hill, Hannah; Muhandes, Samer; Borup, Morten; Mijic, Ana

doi:10.1029/2021wr030778

Cited by 8 publications

(10 citation statements)

References 23 publications

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“…To allow for the sampling of a wider range of future scenarios, surrogate or conceptual models have to be used. Conceptual models used in urban drainage modeling are often virtual‐reservoir models (Cembrano et al., 2004), but other forms of simplified models could be used (e.g., Dobson et al., 2022; Meijer et al., 2018), provided they incorporate a physical‐based description of the UDS and its structure. This means an encoding of the effective surface area, UDS storage capacity, and pumping capacities (if appropriate).…”

Section: Methodsmentioning

confidence: 99%

The Impact of Blue‐Green Infrastructure and Urban Area Densification on the Performance of Real‐Time Control of Sewer Networks

Werf

Kapelan

Langeveld

2023

Water Resources Research

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Urban drainage systems (UDS) are designed to convey wastewater and urban runoff away from populated areas to treat the water and increasingly to recover valuable resources from it. To prevent flooding during rainfall events, combined sewer overflows (CSOs) are installed to discharge the excess of combined wastewater and urban runoff (i.e., stormwater) into a receiving water body. These CSO events can negatively impact the receiving water quality (Owolabi et al., 2022) and can be highly persistent and long-lasting in nature, for example, as in the case of micro-pollutants from CSO events impacting water resources (Mutzner et al., 2022). These effects are projected to be exacerbated by more intense rainfall events resulting from climate change (Gogien et al., 2022) and the expansion and densification of the population in existing urban areas.To minimize the adverse effects of CSOs, blue-green infrastructure in the form of sustainable urban drainage systems (SUDS) is increasingly implemented to revert to the pre-urbanized hydrological conditions by allowing more infiltration in the urban environment (Carter & Fowler, 2008). Allowing significant parts of the urban runoff to infiltrate or to be temporarily stored, alleviates pressure on the drainage system and can therefore significantly reduce the CSO volumes (e.g., Chen et al., 2019). Using these types of nature based solutions are increasingly mandated by legislative bodies. The new EU Urban Wastewater Treatment Directive, for example, explicitly states a preference for green developments and states that gray infrastructure should only be envisaged where absolutely necessary. In future (re)developments of the urban environment, different runoff patterns compared to the current situation are therefore highly likely.Another increasingly popular option to reduce CSOs is the use of Real-Time Control (RTC) procedures. RTC relies on real-time data obtained from the UDS to optimally control the system actuators (pumps and moveable gates) which has the potential to improve the UDS performance by activating redundant storage capacity in the system (Schütze et al., 2004). Various methods have been developed to achieve this, and can broadly be defined as heuristic and real-time optimization control methods (Garcia et al., 2015). Heuristic control methods are usually in the form of rule-based control, where a set of (optimized) rules are defined offline and implemented online.

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Section: Methodsmentioning

confidence: 99%

The Impact of Blue‐Green Infrastructure and Urban Area Densification on the Performance of Real‐Time Control of Sewer Networks

Werf

Kapelan

Langeveld

2023

Water Resources Research

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“…In latest years, the mission of graph partitioning has attained paramount importance throughout diverse domains [15][16][17][18], encompassing network analysis, parallel computing, and system studying. A multitude of methodologies has been put forth to effectively deal with the challenges related to partitioning massive and tricky graphs.…”

Section: -Related Workmentioning

confidence: 99%

Efficient Graph Partitioning using LouvainSplit Algorithm

Javadi,

Aghighi,

Azadbakht

2024

Preprint

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Graph partitioning is essential for uncovering cohesive communities within complex networks. This paper introduces LouvainSplit, an innovative algorithm designed to enhance graph partitioning efficiency and accuracy. LouvainSplit leverages advanced techniques in feature representation, community detection, and evaluation, providing a robust framework for addressing challenges inherent in graph partitioning tasks across diverse domains. At its core, LouvainSplit utilizes a feature pyramid representation approach to extract both basic and summary features from input graphs at multiple granularity levels. This methodology ensures a subtle evaluation of graph data by capturing fundamental graph information alongside intricate structural patterns, thus offering a comprehensive representation of underlying community structures. A key innovation of our approach is the integration of the Louvain algorithm, renowned for its efficacy in community detection. By leveraging pairwise cosine similarities computed from node feature vectors, the Louvain algorithm optimizes modularity iteratively, effectively partitioning graphs into cohesive communities. This iterative process facilitates the identification of significant network structures within complex networks, providing valuable insights into their organizational dynamics. To comprehensively evaluate LouvainSplit's performance, we integrated diverse graph partitioning algorithms, including PyMetis, Genetic Algorithm (GA), DBSCAN, KMeans, OPTICS, and spectral clustering to evaluate the effectiveness of LouvainSplit and using popular evaluation metrics across diverse benchmarks spanning various domains and graphs. The results demonstrate LouvainSplit's superiority in terms of recall score, modularity, and scalability compared to existing methods. Moreover, LouvainSplit maintains competitive average runtime values, ensuring efficient processing even with large-scale datasets.

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“…It brings together a range of software developed over the course of three years on the CAMELLIA project. Urban water processes are based on those presented and validated in the CityWat model (Dobson et al, 2021;Dobson & Mijic, 2020;Dobson, Watson-Hill, Muhandes, Borup, & Mijic, 2022;, while hydrological and agricultural processes are from the CatchWat model (Liu et al, 2022(Liu et al, , 2023. WSIMOD also provides an interface for message passing between different model components, enabling all parts of the water cycle to interact with all other parts.…”

Section: Statement Of Needmentioning

confidence: 99%

Water Systems Integrated Modelling framework, WSIMOD: A Python package for integrated modelling of water quality and quantity across the water cycle

Dobson¹,

Liu²,

Mijic³

2023

JOSS

Self Cite

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The water cycle is highly interconnected; water fluxes in one part depend on physical and human processes throughout. For example, rivers are a water supply, a receiver of wastewater, and an aggregate of many hydrological, biological, and chemical processes. Thus, simulations of the water cycle that have highly constrained boundaries may miss key interactions that create unanticipated impacts or unexpected opportunities (Dobson & Mijic, 2020;Liu, Dobson, & Mijic, 2022). Integrated environmental models aim to resolve the issue of boundary conditions, however they have some key limitations (Rauch et al., 2017), and in particular we find a significant need for a parsimonious, self-contained suite that is accessible and easy to setup.

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A Reduced Complexity Model With Graph Partitioning for Rapid Hydraulic Assessment of Sewer Networks

Cited by 8 publications

References 23 publications

The Impact of Blue‐Green Infrastructure and Urban Area Densification on the Performance of Real‐Time Control of Sewer Networks

The Impact of Blue‐Green Infrastructure and Urban Area Densification on the Performance of Real‐Time Control of Sewer Networks

Efficient Graph Partitioning using LouvainSplit Algorithm

Water Systems Integrated Modelling framework, WSIMOD: A Python package for integrated modelling of water quality and quantity across the water cycle

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