Automatic Calibration Module for an Urban Drainage System Model

Annus, Ivar; Vassiljev, Anatoli; Kändler, Nils; Kaur, Katrin

doi:10.3390/w13101419

Cited by 9 publications

(8 citation statements)

References 31 publications

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“…Table 1 summarizes the main parameters of the model under study. It is a hydrological hydrodynamics-based urban storm water model primarily used to simulate both water quantity and quality during a single rainfall event or continuous rainfall process in cities [29][30][31][32]. The model parameters of SWMM can be divided into two categories: nonempirical parameters and empirical parameters.…”

Section: Swmm Modelmentioning

confidence: 99%

“…2.2.1. SWMM Model The USEPA (United States Environmental Protection Agency) Storm Water Management Model (SWMM) is an urban hydrological model developed in 1971 by the United States Environmental Protection Agency to handle the expanding urban drainage problem.It is a hydrological hydrodynamics-based urban storm water model primarily used to simulate both water quantity and quality during a single rainfall event or continuous rainfall process in cities[29][30][31][32]. The model parameters of SWMM can be divided into two categories: nonempirical parameters and empirical parameters.…”

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confidence: 99%

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Parameter Optimization of SWMM Model Using Integrated Morris and GLUE Methods

Zhong

Wang

Yang

et al. 2022

Water

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The USEPA (United States Environmental Protection Agency) Storm Water Management Model (SWMM) is one of the most extensively implemented numerical models for simulating urban runoff. Parameter optimization is essential for reliable SWMM model simulation results, which are heterogeneously sensitive to a variety of parameters, especially when involving complicated simulation conditions. This study proposed a Genetic Algorithm-based parameter optimization method that combines the Morris screening method with the generalized likelihood uncertainty estimation (GLUE) method. In this integrated methodology framework, the Morris screening method is used to determine the parameters for calibration, the GLUE method is employed to narrow down the range of parameter values, and the Genetic Algorithm is applied to further optimize the model parameters by considering objective constraints. The results show that the set of calibrated parameters, obtained by the integrated Morris and GLUE methods, can reduce the peak error by 9% for a simulation, and then the multi-objective constrained Genetic Algorithm reduces the model parameters’ peak error in the optimization process by up to 6%. During the validation process, the parameter set determined from the combination of both is used to obtain the optimal values of the parameters by the Genetic Algorithm. The proposed integrated method shows superior applicability for different rainfall intensities and rain-type events. These findings imply that the automated calibration of the SWMM model utilizing a Genetic Algorithm based on the combined parameter set of both has enhanced model simulation performance.

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Section: Swmm Modelmentioning

confidence: 99%

mentioning

confidence: 99%

Parameter Optimization of SWMM Model Using Integrated Morris and GLUE Methods

Zhong

Wang

Yang

et al. 2022

Water

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“…The accuracy of the modelling is dependent on the calibration and validation of the UDS model. Although different automatic calibration tools are proposed [35] the calibration of the UDS models remains a challenge and requires a good knowledge of the system, hydraulics and hydrology. In the NOAH project and in the presented case studies the models were calibrated based on two measured rainfall events.…”

Section: Limitationsmentioning

confidence: 99%

Integrated Decision Support System for Pluvial Flood-Resilient Spatial Planning in Urban Areas

Truu

Annus

Roosimägi

et al. 2021

Water

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Flood-resilient spatial planning in urban areas involves designing and implementing structural and nonstructural measures. For the latter, urban planners apply a precautionary principle, which is normally not grounded in the actual performance of the urban drainage system (UDS). This approach, however, fails during weather extremes with heavy precipitation. This paper presents a new concept for reducing pluvial flood risks in the urban planning process. The novelty of the developed planning support system named Extreme Weather Layer (EWL) is that it creates dynamic interlinkages between land developments, the performance of UDS, and other factors that contribute to flood risk. The EWL is built on the digital twin of the existing UDS and delivers an easy-to-use concept, where the end user can analyze hydraulic modelling results interlinked with climate scenarios using the GIS platform. This allows planning specialists to consider land use and soil types in the urban environment to simulate the response of the storm water system and the catchments to different rainfall events. This proposed approach was piloted in Haapsalu (Estonia) and Söderhamn (Sweden). The resulting planning support system, which performs as a set of layers within municipalities’ GIS, allows decision makers to understand and predict the impact of various spatial planning decisions on the pluvial flood risk.

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“…To obtain reliable model results, the SWMM parameter-calibration process should essentially improve system sustainability. UDS parameter calibration is an iterative process that adjusts various model parameters while minimizing and/or maximizing the predefined objectives (e.g., difference between simulated and observed values) [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Development of a Multiobjective Automatic Parameter-Calibration Framework for Urban Drainage Systems

2022

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Urban drainage systems (UDSs) continue to face challenges, despite numerous efforts to improve their sustainability through design, planning, and management. The goal of such initiatives is to avoid and minimize flooding as well as maintain the UDS’s sustainable functionality, which can be analyzed using a stormwater management model (SWMM). In this study, a multiobjective automatic parameter-calibration (MAPC) framework was developed based on the SWMM. It consisted of three steps: sensitivity analysis (Step I), objective selection (Step II), and SWMM parameter calibration (Step III). The proposed MAPC framework was verified using the Yongdap drainage network located in Seoul, South Korea. The resultant MAPC framework demonstrated that the system characteristics (such as percent of impervious area and hillslope) and problems in UDS design, planning, and management can be well reflected by the corresponding model. The MAPC framework proposed in this study can contribute to UDS modeling sustainability.

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Automatic Calibration Module for an Urban Drainage System Model

Cited by 9 publications

References 31 publications

Parameter Optimization of SWMM Model Using Integrated Morris and GLUE Methods

Parameter Optimization of SWMM Model Using Integrated Morris and GLUE Methods

Integrated Decision Support System for Pluvial Flood-Resilient Spatial Planning in Urban Areas

Development of a Multiobjective Automatic Parameter-Calibration Framework for Urban Drainage Systems

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