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Modeling and Simulation of Urban Drainage Systems
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Modeling and Simulation of Urban Drainage Systems

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Urban Water Management".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 18521

Special Issue Editor

Dr. Stefan Kroll

E-Mail Website
Guest Editor
Aquafin NV, Aartselaar, Belgium
Interests: urban drainage modelling; real-time modelling; real-time control; digital twins; machine learning

Special Issue Information

Dear Colleagues,

The use of models is indispensable for the planning, design, upgrade and operation of urban drainage systems.

With recent developments in AI and ML, new, powerful tools have become available for modellers to cover aspects that may have been difficult or impossible to solve using previous existing modelling environments. At the same time, advances in hardware and computing make established computationally intensive models available for tasks that would have entailed prohibitively long simulation durations in the past. This leads to an unprecedented choice of tools for modellers in the field of urban drainage for systems analysis.

For this Special Issue, we would like to encourage the submission of original papers covering the development, calibration and validation of models and tools using both knowledge-driven techniques or the recently re-discovered data-driven alternatives and their application to problems in the field of urban drainage.

Considered applications include, but are not limited to, the design of new systems as well as upgrade of existing ones, but also operational aspects, including real-time simulation and control, data analysis, forecasting and the application of Digital Twins and decision support systems both for water quantity and quality in sewer networks and sustainable and decentralised systems.

Dr. Stefan Kroll
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • hydrodynamic modelling
  • conceptual modelling
  • artificial intelligence (AI)
  • machine learning (ML)
  • data analysis
  • decision support systems
  • real-time control
  • digital twin
  • real-time modelling
  • water quality

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

14 pages, 1907 KiB  
Article
CSO Generator—A Parsimonious Wastewater Quality Model for Combined Sewer Overflows
by Tom Wambecq, Stefan Kroll, Johan Van Assel and Rosalia Delgado
Water 2023, 15(19), 3424; https://doi.org/10.3390/w15193424 - 28 Sep 2023
Cited by 3 | Viewed by 1521
Abstract
Combined sewage overflows (CSOs) are a common consequence of heavy rainfall events and can have significant implications for water quality in receiving waterbodies. With climate change, these events are becoming more frequent and intense, placing greater pressure on aquatic ecosystems. To prevent water [...] Read more.
Combined sewage overflows (CSOs) are a common consequence of heavy rainfall events and can have significant implications for water quality in receiving waterbodies. With climate change, these events are becoming more frequent and intense, placing greater pressure on aquatic ecosystems. To prevent water pollution, it is essential to utilize numerical tools to investigate, forecast, and establish control measures for CSOs. Typically, these tools involve a dynamic model for flow simulation combined with either a detailed model for pollutants or a simplified event mean concentration (EMC) calculation. However, both approaches have drawbacks: a detailed model requires extensive calibration time, while the EMC does not account for system dynamics. To overcome these issues, a novel system was developed that integrates the dynamic nature of the detailed model with the rapid calibration of the EMC. This model employs two distinct concepts for pollution modeling: one for soluble compounds and one for suspended solids. The resulting model was evaluated at multiple locations with varying hydraulic dynamics, demonstrating its potential utility at any location where a dynamic model of the sewer system is available. Full article
(This article belongs to the Special Issue Modeling and Simulation of Urban Drainage Systems)
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13 pages, 2588 KiB  
Article
State Selection and Cost Estimation for Deep Reinforcement Learning-Based Real-Time Control of Urban Drainage System
by Wenchong Tian, Kunlun Xin, Zhiyu Zhang, Zhenliang Liao and Fei Li
Water 2023, 15(8), 1528; https://doi.org/10.3390/w15081528 - 13 Apr 2023
Cited by 7 | Viewed by 2502
Abstract
In recent years, a real-time control method based on deep reinforcement learning (DRL) has been developed for urban combined sewer overflow (CSO) and flooding mitigation and is more advantageous than traditional methods in the context of urban drainage systems (UDSs). Since current studies [...] Read more.
In recent years, a real-time control method based on deep reinforcement learning (DRL) has been developed for urban combined sewer overflow (CSO) and flooding mitigation and is more advantageous than traditional methods in the context of urban drainage systems (UDSs). Since current studies mainly focus on analyzing the feasibility of DRL methods and comparing them with traditional methods, there is still a need to optimize the design and cost of DRL methods. In this study, state selection and cost estimation are employed to analyze the influence of the different states on the performance of DRL methods and provide relevant suggestions for practical applications. A real-world combined UDS is used as an example to develop DRL models with different states. Their control effect and data monitoring costs are then compared. According to the results, the training process for DRL is difficult when using fewer nodes information or water level as the input state. Using both upstream and downstream nodes information as input improves the control effect of DRL. Also, using the information on upstream nodes as the input state is more effective than using downstream nodes; using flow as input is more likely to have a better control effect than using water level, while using both flow and water level cannot significantly further improve the control effect. Because the cost of flow monitoring is higher than water level monitoring, the number of monitoring nodes and the use of flow/water level need to be balanced based on cost-effectiveness. Full article
(This article belongs to the Special Issue Modeling and Simulation of Urban Drainage Systems)
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16 pages, 5193 KiB  
Article
A New Decision Support Tool for Evaluating the Impact of Stormwater Management Systems on Urban Runoff Pollution
by Evi Vinck, Birgit De Bock, Tom Wambecq, Els Liekens and Rosalia Delgado
Water 2023, 15(5), 931; https://doi.org/10.3390/w15050931 - 28 Feb 2023
Cited by 5 | Viewed by 2355
Abstract
Stormwater runoff is often discharged untreated into receiving waters, a process that is widely recognized as a threat to water quality. To protect water bodies, tools are needed to assess the risk of urban runoff pollution. In this work, a new tool is [...] Read more.
Stormwater runoff is often discharged untreated into receiving waters, a process that is widely recognized as a threat to water quality. To protect water bodies, tools are needed to assess the risk of urban runoff pollution. In this work, a new tool is presented that can be used to model the concentration of the most frequent pollutants in urban runoff, i.e., Zn, Cu, Pb, PAH(1)6, TN, and TP, based not only on the surface type but also on other inputs such as the amount of traffic or the building type. The tool also includes a simple model to evaluate the impact of different SUDS types. The water quality model was evaluated by measurement campaigns in separate sewer systems of a few small catchments in Flanders. The model was able to reproduce the observed time-dependent spread in concentrations in a satisfactory manner. Furthermore, the model also allowed for the attribution of differences in heavy metal concentrations in catchments very similar to the building types. These are clear improvements compared to previous model approaches. Full article
(This article belongs to the Special Issue Modeling and Simulation of Urban Drainage Systems)
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23 pages, 4728 KiB  
Article
Calculation of Dry Weather Flows in Pumping Stations to Identify Inflow and Infiltration in Urban Drainage Systems
by Johan Van Assel, Stefan Kroll and Rosalia Delgado
Water 2023, 15(5), 864; https://doi.org/10.3390/w15050864 - 23 Feb 2023
Cited by 3 | Viewed by 3437
Abstract
The performance of most urban drainage systems is adversely affected by unintended connections of groundwater and surface water, often denoted as inflow and infiltration (I&I). Various methods exist to locate and characterise these effects. Yet, it remains difficult to quantify them accurately, especially [...] Read more.
The performance of most urban drainage systems is adversely affected by unintended connections of groundwater and surface water, often denoted as inflow and infiltration (I&I). Various methods exist to locate and characterise these effects. Yet, it remains difficult to quantify them accurately, especially in terms of spatial distribution over a larger drainage area. One of the reasons for this is the lack of sufficient high-quality sewer flow measurements at a high temporal resolution, which would enable the calibration of detailed spatio-temporal relationships between rainfall and I&I flows. In this paper, a methodology is presented for deriving sewer flow time series from operational measurements at pumping stations, and the results from four pilot locations are discussed. It shows the potential of the methodology to be implemented at a large scale and to contribute to a better understanding and remediation of I&I in urban drainage management planning. Full article
(This article belongs to the Special Issue Modeling and Simulation of Urban Drainage Systems)
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19 pages, 3779 KiB  
Article
Optimal Positioning of RTC Actuators and SuDS for Sewer Overflow Mitigation in Urban Drainage Systems
by Marco Eulogi, Sonja Ostojin, Pete Skipworth, Stefan Kroll, James D. Shucksmith and Alma Schellart
Water 2022, 14(23), 3839; https://doi.org/10.3390/w14233839 - 25 Nov 2022
Cited by 10 | Viewed by 3134
Abstract
Real-time control (RTC) and sustainable drainage systems (SuDS) can be implemented simultaneously to enhance the performance of existing urban drainage systems (UDS). However, significant challenges arise when choosing the optimal locations due to hydraulic interactions between the different interventions and the high number [...] Read more.
Real-time control (RTC) and sustainable drainage systems (SuDS) can be implemented simultaneously to enhance the performance of existing urban drainage systems (UDS). However, significant challenges arise when choosing the optimal locations due to hydraulic interactions between the different interventions and the high number of possible configurations. This paper presents a novel optimization–simulation framework to optimize the spatial allocation of flow control devices (FCDs) combined with SuDS for combined sewer overflow (CSO) spill mitigation within UDS. Optimal intervention schemes were identified by a genetic algorithm (GA), combining different numbers of FCDs installed in existing manholes with simplified SuDS implemented in different portions of the catchment. The methodology was tested on two case-study catchments with different characteristics to mitigate CSO spills during synthetic storm events. FCD-SuDS configurations were then validated during continuous rainfall series, resulting in CSO spill volumes reduction ranging between 11% and 45% compared to the baseline networks. The results demonstrate how the GA-based method can efficiently identify optimal placement schemes within UDS characterized by different distributions of in-pipe storage potential as well as hydrological responses to rainfall-runoff events, enhancing the combined benefits of the two decentralized solutions for mitigating CSO spills. Full article
(This article belongs to the Special Issue Modeling and Simulation of Urban Drainage Systems)
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20 pages, 9390 KiB  
Article
Stochastic Determination of Combined Sewer Overflow Loads for Decision-Making Purposes and Operational Follow-Up
by Geert Dirckx, Evi Vinck and Stefan Kroll
Water 2022, 14(10), 1635; https://doi.org/10.3390/w14101635 - 19 May 2022
Cited by 8 | Viewed by 3491
Abstract
Characterizing the emissions and impact of combined sewer overflows (CSOs) remains one of the key challenges in the field of urban wastewater. Considering the large number of existing CSOs, decision-makers need a pragmatic approach that allows fairly easy, hands-on determination of emissions (particularly [...] Read more.
Characterizing the emissions and impact of combined sewer overflows (CSOs) remains one of the key challenges in the field of urban wastewater. Considering the large number of existing CSOs, decision-makers need a pragmatic approach that allows fairly easy, hands-on determination of emissions (particularly loads) without compromising accuracy. This philosophy is incorporated in the Cockle tool presented here, which uses stochastically processed input from a vast amount of pre-registered water quality data (pollutant concentrations) in combination with spill flow time series either generated from hydrodynamic models or converted from monitored overflow water levels. Uncertainty is intrinsically covered by the statistical output range of the reported results. As a fully automated tool, Cockle allows to readily assess emissions within a chosen time frame, facilitating more accurate guidance for further remediation actions and/or mapping of the current state for operational follow-up. Full article
(This article belongs to the Special Issue Modeling and Simulation of Urban Drainage Systems)
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