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Parameter optimization of SWMM for Runoff
Quantity and Quality Calculation in a Eutrophic
Lake Watershed Using a Genetic Algorithm
Presented by :Oktarian Wisnu Lusantono
14143701
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In the recent years, several lagoons in the eastcoast area of Korea polluted by diffuse pollutant
Purpose of this study is for improving Youngrang
Lake water quality, thus it is necessary estimatethe diffuse pollution load from it watershed
This study using SWMM (Huber andDickinson,1988; Tsihrintzis and Hamid,1998)
integrated with genetic algorithm (GA)(Goldberg,1989; Gen and Cheng,1997; Cho etal.,2004)
Background
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SWMM has various blocks, in this studyrunoff block was used to calculate the runoffflow and diffuse pollution loads in Jangcheon
watershed Transport block in SWMM was used to
calculate the transport of runoff flow anddiffuse pollution loads in the channel
Calibration for SWMM using manual andautomatic integrated with GA for optimizingSWMM parameters.
Methods
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Study Flowchart- Parameters setting for the genetic operation
- Observed runoff quantity and quality data input- Generate random individuals of the initial generation
- Parents selection according to the fitness
- Offsprings production by crossover and mutation
- Runoff quantity and quality calculation using SWMM- Evaluation of fitness level by SSQ from observed and calculated values
Complete of a new
generation
The new generation
satisfies termination
criteria?
Determination of optimum parameters of SWMM
Yes
Yes
No
No
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Data
Figure 1. The Yeongrang Lake Watershed (4.7 km2)
Remarks :
Arable land 16.5%
Forest 67.7%
Residental area 3.1%
Residental area 12.6%
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Rainfall Event Date Precipitation (mm) Duration (hr) Dry day prior to storm
05 1 June 30 19.6 5 3.2
05 2 July 8 46 18 3.2
05 3 July 28 29 12.5 14.7
05 4 August 24 96.2 28.2 2.8
Table 1. Surveyed Rainfall Events (2005)
Figure 2. Subcathcments and Channel Network for SWMM
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Result and Discussion
Parameters Ranges
Impervious area Mannings roughness 0.001 ~ 0.013
Pervious area Mannings roughness 0.13 ~ 0.51
Max infiltration rate 87.1 ~ 162.0 mm/hr
Min infiltration rate 3.2 ~ 17.8 mm/hr
Impervious depression storage 0.19 ~ 0.72 mm
Pervious depression storage 0.50 ~ 1.49 mm
Channel roughness coefficient 0.11 ~ 0.2Buildup exponent 0.13 ~ 1.11
Buildup coefficient 0.001 ~ 7.71
Washoff exponent 0.16 ~ 9.83
Washoff coefficient 1.0 ~ 19.6
Table 2. Ranges of SWMM Parameters Determined from GA and SWMM
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Month Diffuse pollution loads (kg/year)BOD (kg) COD (kg) SS (kg) TP (kg)
January 910 3885 33847 23.7
February 582 3416 7018 18.5
March 552 3282 2181 16.6
April 674 3883 13472 23.8
May 850 4318 23269 30.0
June 1117 4657 47015 32.9
Juli 2060 11937 170673 151.6
August 2467 14012 267134 199.3
September 2440 9166 225621 101.8
October 871 4424 42524 29.0
November 932 4085 45640 29.2
December 636 3675 6952 20.9
Total 1.41 x 104 7.07 x 104 8.85 x 105 6.78 x 102
Table 3. Monthly Average Pollution Loads Discharged from the Jangcheon Watershed
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SubcatchmentDiffuse pollution loads (kg/year)
BOD (kg) COD (kg) SS (kg) TP (kg)
1 1.47 x 103 6.73 x 103 9.62 x 103 3.85 x 101
2 2.76 x 103 1.35 x 104 5.29 x 104 1.31x 102
3 2.95 x 103 1.46 x 104 3.99 x 104 1.76 x 102
4 1.84 x 103 7.84 x 104 4.67 x 104 7.12 x 101
5 1.79x 103 1.24 x 104 1.44 x 104 1.24 x 102
6 3.29x 103 1.57 x 104 2.33 x 104 1.37 x 102
Total 1.41 x 104 7.07 x 104 8.85 x 105 6.78 x 102
Table 4. Yearly Average Pollution Loads at Each Subcatchment
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Figure 3. Calibration Result of runoff flow using GA
Figure 4. Calibration Result of BOD using GA
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Figure 5. Calibration Result of COD using GA
Figure 6. Calibration Result of SS using GA
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Figure 7. Calibration Result of TP using GA
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Automatic calibration method for SWMMwas applied in the Jangcheon of YoungrangLake Watershed, the calibration using GA
From SWMM continuous simulation over 5years, pollution loads in the summer seasonconsiderably larger than the other seasons.
Residential and agricultural area estimatedhave pollution loads larger than the othersub-catchments area
Conclusion
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Thank you