Automatic calibration of SWMM using NSGA-III and the effects of delineation scale on an urban catchment

The study aims at calibration of the storm water management model (SWMM) with non-dominated sorting genetic algorithm-III (NSGA-III) for urban catchment in Hyderabad, India. The SWMM parameters calibrated were Manning’s roughness coefficient (N ), depression storage for pervious and impervious areas (DP and Di), sub-catchment width (W ), curve number (CN), drying time (dry) of soil and percentage of imperviousness (I ). The efficacy of calibration was evaluated by comparing the observed and simulated peak flows and runoff using goodness-of-fit indices. The calibration takes into consideration eight event rainfalls resulting in eight calibrated sets. Weights of goodness-of-fit indices were estimated and the best calibrated set was further validated for five continuous rainfalls/ runoffs. Simulated runoff volume and peak runoff over the five continuous rainfalls deviated by 7–22% and 2–20% with respect to observed data. Results indicated that parameters calibrated for an event rainfall could be used for continuous rainfall-runoff modelling. The effect of catchment delineation scale on runoff was also studied. The study indicated that output of the model was sensitive to variation in parameter values of infiltration and imperviousness. doi: 10.2166/hydro.2019.033 s://iwaponline.com/jh/article-pdf/21/5/781/602540/jh0210781.pdf V. Swathi (corresponding author) K. Srinivasa Raju Murari R. R. Varma S. Sai Veena Department of Civil Engineering, Birla Institute of Technology and Science, Pilani-Hyderabad Campus, Hyderabad 500078, India E-mail: vemulaswathi999@gmail.com

[1]  R. Singh.  Watershed planning and management , 2000 .

[2]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[3]  F. Hellweger,et al.  Effects of Spatial Resolution in Urban Hydrologic Simulations , 2010 .

[4]  R. H. Bhesdadiya,et al.  An NSGA-III algorithm for solving multi-objective economic/environmental dispatch problem , 2016 .

[5]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[6]  David R. Maidment,et al.  Handbook of Hydrology , 1993 .

[7]  Ching-Lai Hwang,et al.  Methods for Multiple Attribute Decision Making , 1981 .

[8]  K. Sowmya,et al.  Urban flood vulnerability zoning of Cochin City, southwest coast of India, using remote sensing and GIS , 2014, Natural Hazards.

[9]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[10]  Raghavan Srinivasan,et al.  Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model , 2009 .

[11]  Xuefeng Chu,et al.  Event and Continuous Hydrologic Modeling with HEC-HMS , 2009 .

[12]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.

[13]  Samuel Asumadu-Sarkodie,et al.  A review of renewable energy sources, sustainability issues and climate change mitigation , 2016 .

[14]  Drainage Division,et al.  Criteria for Evaluation of Watershed Models , 1993 .

[15]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[16]  Sreenivasulu Reddy,et al.  A New Car Selection in the Market using TOPSIS Technique , 2014 .

[17]  M. Stenstrom,et al.  Closure to “Automatic Calibration of the US EPA SWMM Model for a Large Urban Catchment” by Janet Barco, Kenneth M. Wong, and Michael K. Stenstrom , 2009 .

[18]  Mousumi Basu,et al.  Economic environmental dispatch using multi-objective differential evolution , 2011, Appl. Soft Comput..

[19]  D. Stephenson.  Selection of Stormwater Model Parameters , 1989 .

[20]  Yves Tramblay,et al.  Assessment of initial soil moisture conditions for event-based rainfall–runoff modelling , 2010 .

[21]  D. Nagesh Kumar,et al.  Multicriterion Analysis in Engineering and Management , 2010 .

[22]  Ning Sun,et al.  Impact of SWMM Catchment Discretization: Case Study in Syracuse, New York , 2014 .

[23]  Bradley L Barnhart,et al.  MOESHA: A Genetic Algorithm for Automatic Calibration and Estimation of Parameter Uncertainty and Sensitivity of Hydrologic Models. , 2017, Transactions of the ASABE.

[24]  S T Khu,et al.  From single-objective to multiple-objective multiple-rainfall events automatic calibration of urban storm water runoff models using genetic algorithms. , 2006, Water science and technology : a journal of the International Association on Water Pollution Research.

[25]  G Chebbo,et al.  Bayesian approach for the calibration of models: application to an urban stormwater pollution model. , 2003, Water science and technology : a journal of the International Association on Water Pollution Research.

[26]  Ismail Abustan,et al.  GIS-based river flood hazard mapping in urban area (a case study in Kayu Ara river basin, Malaysia) , 2010 .

[27]  Maurizio Giugni,et al.  A jazz-based approach for optimal setting of pressure reducing valves in water distribution networks , 2016 .

[28]  John E. Hunt,et al.  Learning using an artificial immune system , 1996 .

[29]  S. Herath,et al.  Modeling of Event and Continuous Flow Hydrographs with HEC–HMS: Case Study in the Kelani River Basin, Sri Lanka , 2014 .

[30]  H. Setälä,et al.  A high resolution application of a stormwater management model (SWMM) using genetic parameter optimization , 2013 .

[31]  R. Maity.  Statistical Methods in Hydrology and Hydroclimatology , 2018, Springer Transactions in Civil and Environmental Engineering.

[32]  Paulin Coulibaly,et al.  Event-based model calibration approaches for selecting representative distributed parameters in semi-urban watersheds , 2018, Advances in Water Resources.

[33]  F. Paola,et al.  A harmony-based calibration tool for urban drainage systems , 2018 .

[34]  Jeffrey G. Arnold,et al.  Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations , 2007 .

[35]  Kyung-sook Choi,et al.  Parameter estimation for urban runoff modelling , 2002 .

[36]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[37]  V. Venugopal,et al.  Wet and dry spell characteristics of global tropical rainfall , 2013 .