Calibration of SWMM Runoff Quality Model with Expert System

Baffaut, Claire; Delleur, J. W.

doi:10.1061/(asce)0733-9496(1990)116:2(247)

Cited by 29 publications

(12 citation statements)

References 6 publications

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“…Therefore, only impervious areas were used by SWMM in simulating weekly TN and TP export. Using the expert methodology proposed by Baffaut and Delleur (), initial values for maximum buildup were calculated using the maximum observed loadings (kg/ha/week) multiplied by a factor of six to achieve an adequate maximum limit. Although a factor of six may not represent a universal value, other than Baffaut and Delleur (), there was limited guidance found on choosing a maximum buildup value.…”

Section: Methodsmentioning

confidence: 99%

“…Using the expert methodology proposed by Baffaut and Delleur (), initial values for maximum buildup were calculated using the maximum observed loadings (kg/ha/week) multiplied by a factor of six to achieve an adequate maximum limit. Although a factor of six may not represent a universal value, other than Baffaut and Delleur (), there was limited guidance found on choosing a maximum buildup value. Washoff coefficients and exponents were taken from Baffaut and Delleur () using an initial buildup rate constant of one.…”

Section: Methodsmentioning

confidence: 99%

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Calibration and Verification of SWMM for Low Impact Development

Rosa

Clausen

Dietz

2015

J American Water Resour Assoc

169

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The Storm Water Management Model was used to simulate runoff and nutrient export from a low impact development (LID) watershed and a watershed using traditional runoff controls. Predictions were compared to observed values. Uncalibrated simulations underpredicted weekly runoff volume and average peak flow rates from the multiple subcatchment LID watershed by over 80%; the single subcatchment traditional watershed had better predictions. Saturated hydraulic conductivity, Manning's n for swales, and initial soil moisture deficit were sensitive parameters. After calibration, prediction of total weekly runoff volume for the LID and traditional watersheds improved to within 12 and 5% of observed values, respectively. For the validation period, predicted total weekly runoff volumes for the LID and traditional watersheds were within 6 and 2% of observed values, respectively. Water quality simulation was less successful, Nash-Sutcliffe coefficients >0.5 for both calibration and validation periods were only achieved for prediction of total nitrogen export from the LID watershed. Simulation of a 100-year, 24-h storm resulted in a runoff coefficient of 0.46 for the LID watershed and 0.59 for the traditional watershed. Results suggest either calibration is needed to improve predictions for LID watersheds or expanded look-up tables for Green-Ampt infiltration parameter values that account for compaction of urban soil and antecedent conditions are needed.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Calibration and Verification of SWMM for Low Impact Development

Rosa

Clausen

Dietz

2015

J American Water Resour Assoc

169

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show abstract

“…For quantity assessment criteria, a model calibration can be considered good if the average RE is within ±10 % and the average ARE is less than 15 % (Baffaut and Delleur 1989;Sriananthakumar and Codner 1992). For quality assessment criteria, Baffaut and Delleur (1990) suggested that a good calibration must have an average ARE less than 20 % while Sivakumar et al (1995) suggested less stringent criteria; good when the average ARE is less than 30 % and satisfactory when the average ARE is less than 60 %. 2.…”

Section: Goodness-of-fit Testmentioning

confidence: 99%

Modelling runoff quantity and quality in tropical urban catchments using Storm Water Management Model

Fai

Yusop

Toriman

2012

Int. J. Environ. Sci. Technol.

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Due to differences in rainfall regimes and management practices, tropical urban catchments are expected to behave differently from temperate catchments in terms of pollutant sources and their transport mechanism. Storm Water Management Model (SWMM) was applied to simulate runoff quantity (peakflow and runoff depth) and quality (total suspended solids and total phosphorous) in residential, commercial and industrial catchments. For each catchment, the model was calibrated using 8-10 storm events and validated using seven new events. The model performance was evaluated based on the relative error, normalized objective function, Nash-Sutcliffe coefficient and 1:1 plots between the simulated and observed values. The calibration and validation results showed good agreement between simulated and measured data. Application of Storm Water Management Model for predicting runoff quantity has been improved by taking into account catchment's antecedent moisture condition. The impervious depression storages obtained for dry and wet conditions were 0.8 and 0.2 mm, respectively. The locally derived build-up and wash-off parameters were used for modelling runoff quality.

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“…The SWMM is a comprehensive hydrological simulation model that is commonly applied to designing the quality and quantity models of runoffs in urban areas (Ackerman and Schiff, 2003;Baffaut and Delleur, 1990;Borah and Bera, 2003;Denault et al, 2006;Maharjan et al, 2009;Tsihrintzis and Hamid, 1998). As understood, even though the RNN can easily build a water level prediction model at the gauged sites, the difficulty in predicting the water levels at an ungauged site is due to its unavailability of real observations for the training and evaluation of a RNN.…”

Section: Storm Water Management Model (Swmm)mentioning

confidence: 99%

Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites

Chiang¹,

Chang²,

Tsai³

et al. 2010

Hydrol. Earth Syst. Sci.

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Abstract. In this research, we propose recurrent neural networks (RNNs) to build a relationship between rainfalls and water level patterns of an urban sewerage system based on historical torrential rain/storm events. The RNN allows signals to propagate in both forward and backward directions, which offers the network dynamic memories. Besides, the information at the current time-step with a feedback operation can yield a time-delay unit that provides internal input information at the next time-step to effectively deal with time-varying systems. The RNN is implemented at both gauged and ungauged sites for 5-, 10-, 15-, and 20-min-ahead water level predictions. The results show that the RNN is capable of learning the nonlinear sewerage system and producing satisfactory predictions at the gauged sites. Concerning the ungauged sites, there are no historical data of water level to support prediction. In order to overcome such problem, a set of synthetic data, generated from a storm water management model (SWMM) under cautious verification process of applicability based on the data from nearby gauging stations, are introduced as the learning target to the training procedure of the RNN and moreover evaluating the performance of the RNN at the ungauged sites. The results demonstrate that the potential role of the SWMM coupled with nearby rainfall and water level information can be of great use in enhancing the capability of the RNN at the ungauged sites. Hence we can conclude that the RNN is an effective and suitable model for successfully predicting the water levels at both gauged and ungauged sites in urban sewerage systems.

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Calibration of SWMM Runoff Quality Model with Expert System

Cited by 29 publications

References 6 publications

Calibration and Verification of SWMM for Low Impact Development

Calibration and Verification of SWMM for Low Impact Development

Modelling runoff quantity and quality in tropical urban catchments using Storm Water Management Model

Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites

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