Automatic Calibration of the U.S. EPA SWMM Model for a Large Urban Catchment

Low impact development (LID) is an important control measure against extreme rainfall events and is widely applied to relieve urban flood disasters. To investigate the effects of LID practices on flooding control under different rainfall scenarios, this paper constructs a rainfall-runoff model based on the storm water management model (SWMM) for a typical residential area in Guangzhou, China. The model is calibrated by using observed rainfall and runoff data. A total of 27 rainfall scenarios are constructed to simulate the change characteristics before and after the LID practices. Also, the projection pursuit method based on a particle swarm optimization (PSO) algorithm is used to assess the flooding characteristics. The results show that the constructed rainfall-runoff model can closely reflect the relationship between rainfall and runoff, with all Nash-Sutcliffe coefficients of efficiency (NSE) exceeding 0.7. It was found from the simulation and assessment of the constructed rainfall scenarios that the changes in rainfall characteristics have a considerable impact on the constructed drainage system and that LID practices can properly control the floods. However, with an increase in rainfall peak coefficient, intensity or duration, the control effects of LID tend to reduce. Particularly in the scenario of relatively high rainfall intensity, the impact of rainfall duration and the rainfall peak coefficient on the LID practices is minor.

Section: Swmm Modelmentioning

confidence: 99%

Evaluating the Effects of Low Impact Development Practices on Urban Flooding under Different Rainfall Intensities

Zhu

Chen

2017

“…The technique is based on the optimization method called the complex method developed by Box [26]. The details of the procedure are shown in Barco et al [27]. In this work, the following parameters have been considered in the calibration process: Manning's coefficients, depression storage for both impervious and pervious area, and the parameters of the Green-Ampt's equation (Suction Head S u , Conductivity K s and Initial Deficit).…”

Section: Swmmmentioning

confidence: 99%

Support Vector Regression for Rainfall-Runoff Modeling in Urban Drainage: A Comparison with the EPA’s Storm Water Management Model

Granata

Gargano

Marinis

2016

130

Abstract:Rainfall-runoff models can be classified into three types: physically based models, conceptual models, and empirical models. In this latter class of models, the catchment is considered as a black box, without any reference to the internal processes that control the transformation of rainfall to runoff. In recent years, some models derived from studies on artificial intelligence have found increasing use. Among these, particular attention should be paid to Support Vector Machines (SVMs). This paper shows a comparative study of rainfall-runoff modeling between a SVM-based approach and the EPA's Storm Water Management Model (SWMM). The SVM is applied in the variant called Support Vector regression (SVR). Two different experimental basins located in the north of Italy have been considered as case studies. Two criteria have been chosen to assess the consistency between the recorded and predicted flow rates: the root-mean square error (RMSE) and the coefficient of determination. The two models showed comparable performance. In particular, both models can properly model the hydrograph shape, the time to peak and the total runoff. The SVR algorithm tends to underestimate the peak discharge, while SWMM tends to overestimate it. SVR shows great potential for applications in the field of urban hydrology, but currently it also has significant limitations regarding the model calibration.

“…The software can be used in the design, analysis, and planning of drainage systems as well as simulating the runoff quantity and quality [20][21][22][23][24][25][26][27][28]. In addition, several attempts have been made to calibrate the parameters required by the software [29,30] and using it in order to compare against machine learning techniques [31].…”

Section: Swmm Softwarementioning

confidence: 99%

Quantifying Roughness Coefficient Uncertainty in Urban Flooding Simulations through a Simplified Methodology

Bellos

Kourtis

Moreno-Rodenas

et al. 2017

Abstract:A methodology is presented which can be used in the evaluation of parametric uncertainty in urban flooding simulation. Due to the fact that such simulations are time consuming, the following methodology is proposed: (a) simplification of the description of the physical process; (b) derivation of a training data set; (c) development of a data-driven surrogate model; (d) use of a forward uncertainty propagation scheme. The simplification comprises the following steps: (a) unit hydrograph derivation using a 2D hydrodynamic model; (b) calculation of the losses in order to determine the effective rainfall depth; (c) flood event simulation using the principle of the proportionality and superposition. The above methodology was implemented in an urban catchment located in the city of Athens, Greece. The model used for the first step of the simplification was FLOW-R2D, whereas the well-known SWMM software (US Environmental Protection Agency, Washington, DC, USA) was used for the second step of the simplification. For the training data set derivation, an ensemble of 100 Unit Hydrographs was derived with the FLOW-R2D model. The parameters which were modified in order to produce this ensemble were the Manning coefficients in the two friction zones (residential and urban open space areas). The surrogate model used to replicate the unit hydrograph derivation, using the Manning coefficients as an input, was based on the Polynomial Chaos Expansion technique. It was found that, although the uncertainties in the derived results have to be taken into account, the proposed methodology can be a fast and efficient way to cope with dynamic flood simulation in an urban catchment.