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

Kim, Seon Woo; Kwon, SangJoo; Jung, Donghwi

doi:10.3390/su14148350

Cited by 8 publications

(3 citation statements)

References 49 publications

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“…This approach significantly reduces the human influence and time consumption associated with manual calibration, thereby enhancing modelling efficiency and credibility. Kim et al established a coupled SWMM underground drainage network multi-objective automatic parameter calibration model for Seoul [14] using a Non-dominated Sorting Harmony Search (NSHS) algorithm. Experimental testing revealed that the calibrated model framework could effectively reflect system characteristics and address issues in pipe design, planning, and management, thereby contributing to simulation research.…”

Section: Introductionmentioning

confidence: 99%

A Coupled Parameter Automation Calibration Module for Urban Stormwater Modelling

Gu,

Sun,

Gao

et al. 2024

Water

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In the context of accelerating urbanisation, the issue of urban stormwater flooding security has garnered increasing attention. Further development of urban stormwater management techniques is imperative to achieve more stable, precise, and expeditious simulation outcomes. The calibration of model parameters, which is a pivotal phase in stormwater simulation endeavours, is hampered by challenges such as substantial subjectivity, time intensiveness, and low efficiency. Therefore, this study introduces a parameter calibration model coupled with the Non-dominated Sorting Genetic Algorithm III (NSGA-III). This model utilises the Nash–Sutcliffe efficiency (NSE) and peak relative error (PE) values for various rainfall events as objective functions to calibrate and assess the study target. The two rainfalls used for rate determination had NSE values greater than 0.9 and absolute PE values less than 0.17; the rainfall used for validation had NSE values greater than 0.9 and absolute PE values less than 0.27. Thus, the results of the model for the rate determination of the parameters are reliable. In addition, the inverted generation distance and hypervolume values indicate that the iterative process of the algorithm during population evolution demonstrated stable iterative outcomes and ensured sound population quality. Both reach relative stability after 40 iterations. In conclusion, the proposed multi-objective parameter calibration model integrated with NSGA-III offers dependable calibration results and robust computational efficacy, presenting novel avenues and perspectives for urban stormwater model parameter calibration and simulation.

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Section: Introductionmentioning

confidence: 99%

A Coupled Parameter Automation Calibration Module for Urban Stormwater Modelling

Gu,

Sun,

Gao

et al. 2024

Water

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“…The interception and infiltration capacities of shrub were found to be greater than those of lawn, and the rainfall intensity and pattern were the major factors that influenced the interception and infiltration of rainwater (Xu et al, 2019). A multiobjective automatic parameter-calibration (MAPC) framework was developed based on the SWMM (Kim et al, 2022). LID modeling is still relatively new, and the understanding of model sensitivity to certain parameters is lacking (Panos et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Parameter Sensitivity Analysis of SWMM model in an Airport Airfield Area

Peng

Zhao

Ouyang

et al. 2023

Preprint

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Storm Water Management Model (SWMM) and Geographic Information System (GIS) can provide prediction and management for airport flood problems. Efficient and accurate acquisition of sensitive parameters is the key to real-time model calibration. Due to the influence of special land types, functional zoning and use requirements of airports, there are many problems in parameter sensitivity analysis, such as large sampling parameters, large amount of calculation, and nonlinear correlation between input and output variables. In this paper, the SWMM model of airport airfield area is built, combining GIS and Python programming technology and using Latin Hypercube sampling, a correlation analysis method is proposed to study whether the input parameters have nonlinear correlation with the output results and its strength, and compared with the improved Morris screening method. The results show that, the sensitivity of parameters is more balanced for the total inflow, there is no very sensitive parameter, and the nonlinear correlation between the parameters and the total inflow is weak. Manning-N is sensitive to average depth, hour of maximum flooding, and time to peak, which indicates that there is a strong nonlinear correlation between them and Manning-N. From the improved Morris screening analysis, it can be seen that there are no highly sensitive parameters for peak flow, and the sensitive parameters are Zero-Imperv and Manning-N. Highly sensitive parameters for time to peak are Manning-N, N-perv, S-Imperv, and N-Imperv. This paper quantitatively analyzes the influence of input parameters of the storm water management model on the output results, effectively identify the important parameters that affecting the output results, and analyze the nonlinear correlation between the input parameters and the output results. The results can greatly improve the accuracy of airport flood model, and provide theoretical guidance for the application and parameter calibration of SWMM in airport.

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“…As a structurally intricate distributed hydrological model, SWMM contains parameters that lack precise physical definitions in the process of model abstraction and simulation. These parameters, such as the impermeable manning coefficient, the depression storage in impermeable areas, and the minimum infiltration rate, are typically determined based on past experiences and may vary with different environmental factors [7][8][9][10]. Consequently, parameter calibration and validation are typically conducted during the urban stormwater runoff simulation process, to ensure the precision and applicability of the simulation results [11].…”

Section: Introductionmentioning

confidence: 99%

Automatic SWMM Parameter Calibration Method Based on the Differential Evolution and Bayesian Optimization Algorithm

Gao,

Liang,

et al. 2023

Water

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In response to the low accuracy exhibited by the Storm Water Management Model (SWMM), we propose an enhanced Differential Evolution and Bayesian Optimization Algorithm (DE-BOA). This algorithm integrates the global search capability of the differential evolution algorithm with the local search capability of the Bayesian optimization algorithm, which enables a more comprehensive exploration of the vector solution space. A comparative analysis of various types of rainfall events is conducted. For model calibration and validation, a drainage subzone in Jinshazhou, Guangzhou City, is selected as the research subject. In total, 20 specific rainfall events are selected, and the DE-BOA algorithm outperforms the manual calibration, the differential evolution algorithm, and the Bayesian optimization algorithm regarding model calibration accuracy. Furthermore, the DE-BOA algorithm exhibits robust adaptability to rainfall events characterized by multiple peaks and higher precipitation levels, with the Nash–Sutcliffe efficiency coefficient values surpassing 0.90. This study’s findings could hold significant reference value for dynamically updating model parameters, thereby enhancing the model simulation performance and improving the accuracy of the urban intelligent water management platform.

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

Cited by 8 publications

References 49 publications

A Coupled Parameter Automation Calibration Module for Urban Stormwater Modelling

A Coupled Parameter Automation Calibration Module for Urban Stormwater Modelling

Parameter Sensitivity Analysis of SWMM model in an Airport Airfield Area

Automatic SWMM Parameter Calibration Method Based on the Differential Evolution and Bayesian Optimization Algorithm

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