Impact-based integrated real-time control for improvement of the Dommel River water quality

Langeveld, Jeroen; Benedetti, Lorenzo; Klein, J.J.M. de; Amerlinck, Youri; Nieuwenhuijzen, A. van; Flameling, Tony; Zanten, Oscar van; Weijers, Stefan

doi:10.1080/1573062x.2013.820332

Cited by 52 publications

(50 citation statements)

References 23 publications

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“…Additionally, RTC can also include the status of the receiving water body and other variables (such as energy consumption) in the optimisation scheme, as in the modelling studies presented by Vanrolleghem et al (2005), Fu et al (2008) and Langeveld et al (2013), who used multi-objective functions to optimise integrated wastewater systems.…”

Section: Introductionmentioning

confidence: 99%

A generalised Dynamic Overflow Risk Assessment (DORA) for Real Time Control of urban drainage systems

Vezzaro

Grum

2014

Journal of Hydrology

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An innovative and generalised approach to the integrated real time control of urban drainage systems is presented. The Dynamic Overflow Risk Assessment (DORA) strategy aims to minimise the expected Combined Sewer Overflow (CSO) risk by considering (i) the water volume presently stored in the drainage network, (ii) the expected runoff volume (calculated by radar-based nowcast models) and -most important -(iii) the estimated uncertainty of the runoff forecasts. The inclusion of uncertainty allows for a more confident use of Real Time Control (RTC). Overflow risk is calculated by a flexible function which allows for the prioritisation of the discharge points according to their sensitivity and intended use. DORA was tested on a hypothetical example inspired by the main catchment in the city of Aarhus (Denmark). An analysis of DORA's performance over a range of events with different return periods, using a simple conceptual model, is presented. Compared to a traditional local control approach, DORA contributed to reduce CSO volumes from the most sensitive points while reducing total CSO volumes discharged from the catchment. Additionally, the results show that the inclusion of forecasts and their uncertainty contributed to further improving the performance of drainage systems. The results of this paper will contribute to the wider usage of global RTC methods in the management of urban drainage networks.

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Section: Introductionmentioning

confidence: 99%

A generalised Dynamic Overflow Risk Assessment (DORA) for Real Time Control of urban drainage systems

Vezzaro

Grum

2014

Journal of Hydrology

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“…The WWTP operator provided water quantity and quality data of the influent-effluent. More information about the data set can be found at [9]. In total, three years of data were obtained (2011)(2012)(2013).…”

Section: System and Data Descriptionmentioning

confidence: 99%

“…A total of ~200 CSO structures were reduced to 35 clusters representing the spatial locations of the most relevant discharge structures. The WWTP model's characteristics and calibration are described in detail in [9]. This model represents three biological lines and a stormbypass section.…”

Section: Description Of the Model Structurementioning

confidence: 99%

“…A previous version can be found at [9] along with a detailed description of the monitoring data needed for its development and the calibration process (in a dataset from 2001 to 2010). The ICM consists of: An urban drainage routine, which was conceptualised as a set of lumped rainfall-runoff models representing the 29 urban systems of the Dommel area.…”

Section: Description Of the Model Structurementioning

confidence: 99%

“…Nevertheless, when modelling receiving water quality dynamics, urban drainage simulators are often conceptualised through spatially lumped model structures [9,10]. Lumped urban rainfall-runoff models reduce significantly the simulator's computational cost.…”

Section: Introductionmentioning

confidence: 99%

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Impact of Spatiotemporal Characteristics of Rainfall Inputs on Integrated Catchment Dissolved Oxygen Simulations

Moreno-Rodenas

Cecinati

Langeveld

et al. 2017

Water

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Integrated Catchment Modelling aims to simulate jointly urban drainage systems, wastewater treatment plant and rivers. The effect of rainfall input uncertainties in the modelling of individual urban drainage systems has been discussed in several studies already. However, this influence changes when simultaneously simulating several urban drainage subsystems and their impact on receiving water quality. This study investigates the effect of the characteristics of rainfall inputs on a large-scale integrated catchment simulator for dissolved oxygen predictions in the River Dommel (The Netherlands). Rainfall products were generated with varying time-aggregation (10, 30 and 60 min) deriving from different sources of data with increasing spatial information: (1) Homogeneous rainfall from a single rain gauge; (2) block kriging from 13 rain gauges; (3) averaged C-Band radar estimation and (4) kriging with external drift combining radar and rain gauge data with change of spatial support. The influence of the different rainfall inputs was observed at combined sewer overflows (CSO) and dissolved oxygen (DO) dynamics in the river. Comparison of the simulations with river monitoring data showed a low sensitivity to temporal aggregation of rainfall inputs and a relevant impact of the spatial scale with a link to the storm characteristics to CSO and DO concentration in the receiving water.

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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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Impact-based integrated real-time control for improvement of the Dommel River water quality

Cited by 52 publications

References 23 publications

A generalised Dynamic Overflow Risk Assessment (DORA) for Real Time Control of urban drainage systems

A generalised Dynamic Overflow Risk Assessment (DORA) for Real Time Control of urban drainage systems

Impact of Spatiotemporal Characteristics of Rainfall Inputs on Integrated Catchment Dissolved Oxygen Simulations

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

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