Transcript Data treatment EDIC-CEDEC Model - uni

Data treatment
EDIC-CEDEC Model
Thorsten Arnold
Aufarbeitung von GIS-Daten
für Import nach C++
Problem:
- Parzellas belong to multiple
Irrigation sectors
Solution:
- Identify parcels belonging to
Multiple sectors
- Assign parcel to irrigation sector
With largest relation
New problems …
- Roads and rivers defined as
poligons, just like plots …
Processing of Model output
Model Coupling
& Sensitivity Analysis
Assumptions on external
variables
(„World Scenario“)
PA
Mod A
PA ↔ C
PA ↔ B
Mod C
Mod B
PB
PB↔C
PC
Another model setup …
Same problem for sensitivity analysis ?!?
Mod B
Mod A
XML - Kernel
GUI
Mod C
Data
WaSiM
Channel System
Looking Foreward …
one option
Economic
CropWAT
XML - Kernel
GUI
Agents
Data
Looking Foreward …
another option
Economic
WaSiM
CropWAT
XML - Kernel
GUI
Agents
Data
Calibration & Sensitivity
Input
Model as
„blackbox“
Output
Realization of
random variable Y
Summary Statistics:
Distributed input data X
<Y (ς) > = ∫ gς (X,P) p(X,P) dX
pdf :
p (X) = p (X1, X2, …, Xi)
(assumed to be known)
Ω
Y= f( X,P )
With:
X
Y
Ω
ς
input vector
ouput vector
k-dim space of
input factors
moment
Parameter space & Response surface
How do changes in P affect model outputs Y ( P )?
Model results sensitive
to both parameters P1 and P2
 Model results do not
depend on one parameter P1.
(no „turning importance“! )
 Is model redundant in P1 ?
(check „reducing importance“! )
Sensitivity
Local sensitivity in parameters
 Importance for calibration
Gradient
P1 (-1, 1.4)
dY
 dY

grad(Y ( P))  
( P),
( P) 
dP2
 dP1

P2 (0.3,0) P3 (-0.1, -0.7)
 Sensitivity to parameters changes with P !
 Problem: How does my „response surface“ look like?
Sensitivity (V): Screening
Numeric screening experiments
• Control experiment
Vary no factors: baseline run Y (P), with P = [P1, P2, …, PN]
• One-at-a-time (OAT) screening
Vary one factor Pi  Pi + Δ ;
compare results Y (P, Pi) to control experiment Y (P)
• Factorial experiment
Vary all factors at the same time
(random or quasi-random representative of P from pdf,
such as Latin Hypercube)
• Fractional Factorial experiment
Vary many factors Pi,
Choose intelligent methods to save run-time
Sensitivity (V)
Moris‘s OAT design
Var ( Y (P))
Dynamic parameters
Δ P4
x
ΔP1
x
Increasing
dynamic
influence
(interaction,
nonlinearity)
Linear parameters
ΔP2
x
ΔP5
x
ΔP6
x
ΔP3
x
Mean ( Y (P) )
Sensitivity
& Model coupling
• How does the sensitivity of one model
effect the output of other models?
• Which input data / parameter are
responsible for most ouput variation /
output uncertainty of each module?
• In a coupled model, how can be dealt with
parameter sensitivity in order to minimize
output uncertainty?