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Data treatment EDIC-CEDEC Model

Data treatment EDIC-CEDEC Model. Thorsten Arnold. Problem : - Parzellas belong to multiple Irrigation sectors. Solution: Identify parcels belonging to Multiple sectors - Assign parcel to irrigation sector With largest relation. New problems …

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Data treatment EDIC-CEDEC Model

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  1. Data treatmentEDIC-CEDEC Model Thorsten Arnold

  2. 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 … Aufarbeitung von GIS-Datenfür Import nach C++

  3. Processing of Model output

  4. Assumptions on external variables („World Scenario“) PA Mod A Mod C Mod B PA↔ C PA ↔ B PB↔C PB PC Model Coupling & Sensitivity Analysis

  5. Mod B Mod A Mod C Data XML - Kernel GUI Another model setup …Same problem for sensitivity analysis ?!?

  6. Economic Agents WaSiM CropWAT Data XML - Kernel GUI Looking Foreward … one option Channel System

  7. Economic Agents WaSiM CropWAT Data XML - Kernel GUI Looking Foreward … another option Channel System

  8. Model as „blackbox“ Input Output With: X input vectorY ouput vectorΩk-dim space of input factorsςmoment Realization of random variable Y Summary Statistics: <Y (ς) > = ∫ gς (X,P) p(X,P) dX Ω Calibration & Sensitivity Y= f( X,P ) Distributed input data X pdf : p (X) = p (X1, X2, …, Xi) (assumed to be known)

  9. Parameter space & Response surface How do changes in P affect model outputs Y ( P )? • Model results do not depend on one parameter P1. (no „turning importance“! ) • Is model redundant in P1 ?(check „reducing importance“! ) • Model results sensitive to both parameters P1 and P2

  10. Gradient Sensitivity Local sensitivity in parameters  Importance for calibration P1(-1, 1.4) P2(0.3,0) P3 (-0.1,-0.7)  Sensitivity to parameters changes with P !  Problem: How does my „response surface“ look like?

  11. Sensitivity (V): ScreeningNumeric screening experiments • Control experimentVary no factors: baseline run Y (P), with P = [P1, P2, …, PN] • One-at-a-time (OAT) screeningVary 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

  12. Var ( Y (P)) Mean ( Y (P) ) Sensitivity (V)Moris‘s OAT design Dynamic parameters ΔP4x ΔP1x Increasing dynamic influence (interaction, nonlinearity) Linear parameters ΔP5x ΔP6x ΔP2x ΔP3x

  13. 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?

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