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8/19/2019 Qual2kw New

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QUAL2Kw

Worksheet “Headwater” 1. (Discharge Q) 2. Parameter

2.1 (Temperature),2.2 (Conductivity),

2.3 (Dissolved Oxygen),2.4 (CBODfast),2.5 - (NH4-Nitrogen),

2.6 - ( NO3-Nitrogen),

2.7 Total phosphorus (TP),

2.8 (Pathogen)

2.9 - ( pH) Worksheet “ Reach ”

1. (Cross section) 2. (channel slope)3. (side slope) ( - ) 4. (bottom width) 5. , Reach ( )6. Reach ( )

Worksheet “Air Temperature”

1. Reach ( )

Worksheet “ Dew Point Temperature ”

1. Reach ( )

Worksheet “Wind speed ”

Input Data


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1.

Worksheet “Cloud cover”

1.

Worksheet “Shade”

1.

Worksheet “ Point Source ”

1.

- ,- / / - - (5, 10 )

2. - ,- BOD / ,

- . ./ -

Worksheet “Diffuse Sources”

1. 1.5 . Reach2. ( )

3. ( . / ) ( )

4.

Worksheet 1. Hydraulics Data, 2. Temperature Data, 3. WQ Data,4. WQ Data Min, 5. WQ Data Max (Tab worksheet

)

Input Data ( )


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1. Input Data QUAL2Kw Run 2 Run VBA Run Fortran ( Run Fortran ) 1 2

Fitness cell G3 worksheet “Rates”

2. QUAL2Kw Output Data 2(Tab Worksheet ) (Tab Worksheet )Parameter QUAL2Kw ( )

3. Root Mean Square Error

(Print out) 4. Worksheet

“Rate” 4.1 Output parameter

1 2 ( Worksheet “Rates”)code parameter cell

1. Fast CBOD: 2. Organic N: 3. Ammonium:

4. Nitrate: 5. Organic P: 6. Pathogen

Process

Run Auto-cal

(Goldberg & Michalewicz)Run

Auto-cal

Cell G3


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4.2 Parameter Column “E” Dropdown list Yes Max Min Column “F”

Column “G”

Max

Min GA search

Yes GA search

No

Run Auto-cal


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QUAL2Kw GA

QUAL2Kw ( Genetic Algorithm ) 8

1 (Initial population )

2 (Fitness value )

3 (Selection )

4 (Crossover ) (Mutation )

5 (Number of offspring)

6 (Replacement)

7 (Numberof generation)

8 End the Optimization

Yes

Yes

No

No


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1 (Initial population )

2 (Fitness value )

(Fitness value ) (Root mean squared error )

3 (Selection ) 2

4 (Crossover ) (Mutation ) 3

( gene) (offspring)

2

(Mutation)

5 (Number of offspring) 6 3

6 (Replacement)

7 (Number of generation)

Generations termination criteria 8 2

100

300


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8 End the Optimization

Optimized

4.4 Run Auto-cal (Goldberg & Michalewicz) 1 GA Charbonneau & Knapp

Goldberg & Michalewicz

(GA Operators )

GA Operator *

(Charbonneau & Knapp)

GA Operator**

(Goldberg & Michalewicz )

1. (selection operator)1.1 full generational replacement

1.2 steady-state-replace-random

1.3 steady-state-replace-worst

1. (selection operator)1.1 Proportional Selection 1.2 Rank Selection

1.3 Tournament Selection

2. (crossover operator) 2.1 one-point crossover

2.2 two-point crossover

2.3 equal probability of either one-point or two-point

crossover

2.4 uniform crossover

2.5 equal probability of either one-point, two-point, or

uniform2.6 arithmetic crossover

2.7 equal probability of either one-point, two-point,

uniform, or arithmetic

2. (crossover operator)

2.1 one-point crossover 2.2 two-point crossover 2.3 uniform crossover


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(GA Operators ) ( )

GA Operator *

(Charbonneau & Knapp)

GA Operator**

(Goldberg & Michalewicz )

3. (Mutation Operator) 3.1 one-point mutation, fixed rate

3.2 one-point, adjustable rate based on fitness

3.3 one-point, adjustable rate based on distance

3.4 one-point or creep, fixed rate

3.5 one-point or creep, adjustable rate based on fitness

3.6 one-point or creep, adjustable rate based on distance

3. (MutationOperator)

3.1 conventional mutation

3.2 uniform mutation

3.3 modified uniform mutation 3.4 non-uniform mutation

User Interface

GA Operator

Charbonneau & Knapp GA Operator Goldberg & Michalewicz

code 3 Operator Run Auto – cal GA

operator Module Autocal_algorithm


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(GA)

(Genetic Algorithm)

(The Survival of The Fittest) (Charles Darwin, 1809-1882) ( J o h n H o l l a n d ) . .1960-1980

GA Operator Goldberg & Michalewicz

3

1. (Selection) (Parent)

(Population)

(Reproduction)

(Fitness) 3

1.1 Proportional selection probabilities of

selection (Pi) Pi - 1.2 Rank selection

1.3 Tournament selection 2. (Crossover) gene (offspring )) 3

2.1 one-point Crossover

2.2 two-point Crossover

2.3 uniform Crossover

3. (Mutation) 2 chromosome gene

gene 0 1 1 0 4 3.1 conventional Mutation 0 1 1 03.2 Uniform Mutation [0.1, 0.1,

0.12,…] Mutate gene

3.3 Modified uniform Mutation Mutate gene


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3.4 Non- uniform Mutation Mutate Generation ( fitness Optimize )

(Selection operator) Charbonneau and Knapp 1.1 full generational replacement

1.2 steady-state-replace-random

1.3 steady-state-replace-worst

(** crossover mutation Goldberg and Michalewicz)

Output 2 1. Run VBA Run Fortran (Output)

worksheet 1.1 Source Summary

1.2 Temperature Output

1.3 WQ Output

1.4 WQ Min

1.5 WQ Max

( Parameter 1. Fast CBOD, 2. Organic N, 3. Ammonium, 4. Nitrate:5. Organic P, 6.Pathogen)

Output

Worksheet


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2. Run VBA Run Fortran (Output) worksheet (Worksheet output )

2.1 CBOD fast 2.2 Organic N

2.3 NH3 2.4 NO3

2.5 Organic P 2.6 Pathogen

2.7 pH

Worksheet


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Module ( )

1. Module Autocal_rates Column B (B9:B130)

worksheet “Rates” Call PIKAIA_Rates Module Autocal_driver

2. Module Autocal_algorithm ( G A o p e r a t o r ) c o n v e r t

F O RT R A N 7 7 VBA ( c o m m e n t FORTRAN 77)

3. Module Autocal_runq2k Output worksheet ( Source Summary ,

Hydraulics Summary , Temperature Output , WQ Output , WQ Min , WQ Max , Sediment Fluxes ,

Diel Water Column , Diel Hyporheic Diel Fluxes ) RUN Auto-cal callModule Autocal_runq2k ( Module Autocal_runq2k path “qual2kw5.exe” compile FORTRAN Path Qual2kw5.exe RUN FORTRAN )

RUN FORTRAN = Module Autocal_runq2k RUN Auto-cal = Module Autocal_driver RUN VBA = Sub calc Module AMainProgram

Qual2kw

Worksheet “Rates”

np = ctrl (1) = Population number [default np = 100]ngen = ctrl (2) = Number of Generation [default ngen = 500]

nd = ctrl (3) = Encoding accuracy [default nd = 5] pcross = ctrl (4) = Crossover rate [default pcross = 0.85]imut = ctrl (5) = Mutation mode [default imut = 2]

pmut = ctrl (6) = Initial mutation rate [default pmut = 0.005] pmutmn = ctrl (7) = Minimum mutation rate [default pmutmn = 0.0001] pmutmx = ctrl (8) = Maximum mutation rate [default pmutmx = 0.01]fdit = ctrl (9) = Fitness differential [default fdit = 1.0]irep = ctrl (10) = Reproduction plans [default irep = 1]ielite = ctrl (11) = Elitism [default ielite = 1]ivrb = ctrl (12) = Verbose mode [default ivrb = 0]

GA search fitness value (cell “G3”)


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ph = phenotype ( term )gn = genotype ( term GA) ( Encoding)np = Population size (number of populations = search

)ngen = number of generations (1 Generation 3 (selection, crossover,mutation ) GA)rnkpop = Rank population

Element InternalVariable TypeLegal

ValuesDefaultValues

1 np Integer ≤ 128 1002 ngen Integer < ∞ 5003 nd Integer ≤ 6 5

4 pcross Real 0.0 ≤ pcross ≤ 1.0 0.855 imut Integer 1,2 26 pmut Real 0.0 ≤ pmut ≤ 1.0 0.0057 pmutmn Real 0.0 ≤ pmutmn ≤ 1.0 0.00058 pmutmx Real 0.0 ≤ pmutmx ≤ 1.0 0.259 fdit Real 0.0 ≤ fdit ≤ 1.0 1.0

10 irep Integer 1,2,3 111 ielite Integer 0,1 112 ivrb Integer 0,1,2 0

Internal arrays

Array Type True size Active size DescriptionOldph Real (32,128) (n,np) Main population

Newph Real (32,128) (n,np) New populationFitns Real (128) (np) Fitness valuesJfit Integer (128) (np) Fitness-based rankifit Integer (128) (np) Rank key index

ff = fitness function (G3 )

pikaia = GA Fortran Charbonneau& Knapp

Module Autocal_Algorithm convert FORTRAN VBA comment FORTRAN

FORTRAN

.gt. =

.lt. =

.ne. =


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GAParent = Breed = Offspring = Generation = 1 3 1.selection

2. Crossover 3.mutationPopulation = search

Ka =

(Objective Function)

q

im

j

m

jq

i

m jOi j Pi

m

jOi

WiWi x f

1

1

2

1

12

1

,,

,

1)(

Oi, j = observed valuePi, j = predicted valuem = number of pairs of predicted and observed valuesWi = weighting factor

q = number of different state variables (e.g. dissolvedoxygen, pH, nutrient concentrations,etc.) included in thereciprocal of the weighted normalized RMSE.

i = number of reach j = number of point and non-point source

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