udig%jgrasstools.installaon. …latemar.science.unitn.it/lode/objectmodelingsystem/...2013/10/17...

•  Udig-­‐JGrasstools  installa0on  

•  JAMI,  temperature  interpola0on  applica0on    

•  NewAge  Rainfall-­‐Runoff  model  applica0on  

•  NewAge  Rainfall-­‐Runoff  model  PSO  calibra0on  

•  NewAge  Rainfall-­‐Runoff  model  LUCA  calibra0on  

PART 1

Objectives: 1.  Run JAMI OMS3 NewAge model component for air temperature

interpolation;

2.  Plot the interpolated variables and compare them with the measures (scatter)

How JAMI works…

JAMI (Just Another Meteo Interpolator)

•  Split in altimetric band

How JAMI works…

JAMI (Just Another Meteo Interpolator)

How JAMI works…

•  Split in altimetric band

•  Look for the closer stations

JAMI (Just Another Meteo Interpolator)

How JAMI works…

•  Split in altimetric band

•  Look for the closer stations

•  Interpolate according to the variable and number of stations

JAMI (Just Another Meteo Interpolator)

1

2

3

4

JAMI (Just Another Meteo Interpolator)

OMS3 Component

Areas Reader

Altmetry Reader

Stations Reader

Basin Reader

Data Reader

JAMI (Just Another Meteo Interpolator)

What energy and altimetry files are?

Area file Altimetry file

JAMI (Just Another Meteo Interpolator)

PART 2

Objectives: 1.  Run NewAge rainfall runoff model

2.  Plot the simulated variables (timeseries)

NewAge runoff prodiction and routing components

PART 3

Objectives: 1.  NewAge rainfall runoff model calibration by using PSO

2.  NewAge rainfall runoff model calibration by using LUCA

What is PSO?

Mono  and  Mul0  Objec0ve  Calibra0on  

Ispra - 24 June 2011

Example  2  

Mono  and  Mul0  Objec0ve  Calibra0on  

2) Optimization Algorithm

1) Objective Functions to optimize

1) Objectives Functions to optimize:

•  Nash-Sutcliffe

•  Pbias

•  RMSE

•  KGE

•  FHF

•  FLF

Mono  and  Mul0  Objec0ve  Calibra0on  

Mono  and  Mul0  Objec0ve  Calibra0on  2) Optimization Algorithms:

Par0cle  Swarm  Op0miza0on  

Amalgam  

SCE  

Mono  and  Mul0  Objec0ve  Calibra0on  2) Optimization Algorithms:

Par0cle  Swarm  Op0miza0on  

Amalgam  

SCE  

2  1  

Coopera0on  example    Adapted

 from

 Maurice.Clerc@

WriteM

e.com  

2  1  

Coopera0on  example  

Parameter  space  

Par0cles  

Veloci0es   Objec0ve  func0on  

 Adapted

 from

 Maurice.Clerc@

WriteM

e.com  

2  1  

Coopera0on  example  

Parameter  space  

Par0cles  

Veloci0es   Objec0ve  func0on  

 Adapted

 from

 Maurice.Clerc@

WriteM

e.com  

We love animals, is just an example

PSO  Algorithm  

Personal    influence  

Social  influence  

Iner0a  

Start

Initialize particles with random position and velocity vectors.

For each particle’s position (xik)

evaluate fitness

If fitness f(xik) is better than

fitness f(pik-1) then pi

k-1= xik

Set best of pik as pg

k

Loop

unt

il st

oppi

ng

crite

ria is

sat

isfie

d

Stop: giving pgk, optimal solution.

PSO  Algorithm  

Update particles velocity and position

PSO  Algorithm  

HOW?  

Uniform  distribu0on  LHS  

Start

Initialize particles with random position and velocity vectors.

For each particle’s position (xik)

evaluate fitness

If fitness f(xik) is better than

fitness f(pik-1) then pi

k-1= xik

Set best of pik as pg

k

Loop

unt

il st

oppi

ng

crite

ria is

sat

isfie

d

Stop: giving pgk, optimal solution.

Update particles velocity and position

PSO  Algorithm  

HOW?  

TOPOLOGY?  

Uniform  distribu0on  LHS  

Start

Initialize particles with random position and velocity vectors.

For each particle’s position (xik)

evaluate fitness

If fitness f(xik) is better than

fitness f(pik-1) then pi

k-1= xik

Set best of pik as pg

k

Loop

unt

il st

oppi

ng

crite

ria is

sat

isfie

d

Stop: giving pgk, optimal solution.

Update particles velocity and position

The  circular  neighbourhood  

1  

5  

7  

6   4  

3  

8   2  

 Adapted

 from

 Maurice.Clerc@

WriteM

e.com  

The  circular  neighbourhood  

1  

5  

7  

6   4  

3  

8   2  

 Adapted

 from

 Maurice.Clerc@

WriteM

e.com  

The  circular  neighbourhood  

1  

5  

7  

6   4  

3  

8   2  

 Adapted

 from

 Maurice.Clerc@

WriteM

e.com  

LUCA, Let Us CAlibrate

Hay,  L.E.,  Umemoto,  M.,  (2006)  Mul$ple-­‐objec$ve  stepwise  calibra$on  using  Luca:  U.S.  Geological  Survey  Open-­‐File  Report  2006-­‐1323,  25p.    Hay,  L.E.,  Leavesley,  G.H.,  Clark,  M.P.,  Markstrom,  S.L.,  Viger,  R.J.,  and  Umemoto,  M.  (2006).  Step-­‐wise,  mul$ple-­‐objec$ve  calibra$on  of  a  hydrological  model  for  a  snowmelt-­‐dominated  basin.  Journal  of  the  American  Water  Resources  Associa0on.    

one  or  more  steps  execu0on(s)  

 selec0on  of  parameters  from  a  given  distribu0on    

shuffled  complex  evelu0on  SCE

KEY-WORDS

STEP(S)

ROUND(S)

LUCA, Let Us CAlibrate

1) The calibration proceeds one step at a time.

LUCA, Let Us CAlibrate

LUCA, Let Us CAlibrate

1) The calibration proceeds one step at a time.

2) After completing a step, the calibrated values of the parameters passed into the next step.

1) The calibration proceeds one step at a time.

2) After completing a step, the calibrated values of the parameters passed into the next step. 3) This is repeated until all steps are executed

LUCA, Let Us CAlibrate

1) The calibration proceeds one step at a time.

2) After completing a step, the calibrated values of the parameters passed into the next step. 3) This is repeated until all steps are executed 4) All the n steps are repeated #R

LUCA, Let Us CAlibrate