Sloboda Prague

Information about Sloboda Prague

Published on September 25, 2007

Author: Dabby

Source: authorstream.com

Content

Using Bayesian Belief Network to Calibrate Environmental Process Models:  Using Bayesian Belief Network to Calibrate Environmental Process Models Markiyan Sloboda, David Swayne, Vimal Sharma Prague, ISESS 2007 Presentation Structure:  Presentation Structure Introduction Environmental Models and Bayesian Networks Calibration Using the Bayesian Network A Simple Example Results and Analysis Conclusions and Future Work Introduction:  Introduction Environmental models are important tools for environmental assessment and management Almost all environmental models require computationally expensive calibration Manual and auto calibration methodology Environmental Models:  Environmental Models Non-point source (NPS) models overview Classical calibration of environmental models Analysis of the calibration The Bayesian Network:  The Bayesian Network A structure called a Bayesian network is used to represent dependence between variables and to give a concise specification of the joint probability distribution The Bayesian network captures believed relations between set of variables, which are relevant to some problem. Monte Carlo simulations Store and Forward Approach in the NPS Modelling:  Store and Forward Approach in the NPS Modelling Calibration Using the Bayesian Network:  Calibration Using the Bayesian Network Building a 'reservoir' of search attempts Populate the Bayesian Network running many simulations Environmental models have parameters, that have a certain probability distribution Calibration Using the Bayesian Network:  Calibration Using the Bayesian Network Search within the Bayesian network using targeting search is much more efficient than a regular search used to calibrate a model Parallelization and high-performance computing is used to construct the Bayesian Network Calibration Using the Bayesian Network:  Calibration Using the Bayesian Network The network will predict the value of the calibration parameter by calculating their expected value from the belief propagation For multi-parameter calculation, the correct values of the calibration parameters is a system of K equations with K unknowns (possibly non-linear) from which the 'correct' c parameters must be derived The GAMES Model – a Simple Example:  The GAMES Model – a Simple Example The Guelph model for evaluating the effects of Agricultural Management systems on Erosion and Sedimentation (GAMES) Watershed is discretized into field sized elements Can be used for annual or seasonal assessments The GAMES Model – a Simple Example:  The GAMES Model – a Simple Example Erosion Component is Based on the Universal Soil Loss Equation (USLE) Sediment yield delivered from a field The GAMES Model – a Simple Example:  The GAMES Model – a Simple Example Delivery ratio is the percentage of soil loss delivered downstream Seasonal version of the USLE uses seasonal values for the factors Stratford Avon Watershed:  Stratford Avon Watershed Stratford Avon Watershed Drainage Network DistributedFramework:  Distributed Framework Testing Example Using the SHARCNET:  Testing Example Using the SHARCNET The SHARCNET stands for the Shared Hierarchical Academic Computing Network. Established in 2000 The SHARCNET network can be physically described as a 10G WAN between 8 sites Parallel algorithm for populating the Bayesian network was compared to the earlier proposed algorithm Testing Example Using the SHARCNET:  Testing Example Using the SHARCNET Time necessary to populate the Bayesian network Testing Example Using the SHARCNET:  Testing Example Using the SHARCNET Results and Analysis:  Results and Analysis Calibration of the GAMES model is a minimization of the difference between the predicted total sediment load and the observed load of the watershed constructed Instead of estimating each time the α parameter, the α distribution generated is considered, in this case a logarithmic distribution Results and Analysis:  Results and Analysis The populated network, by the definition will be able to generate the calibrated value for the parameter, by just simply specifying the predicted total sediment load The advantage of this approach is that once the Bayesian network was created it will not be reconstructed to get the calibrated value of α for a specific scenario, which is used over the watershed, unlike, in case of standard calibration procedure Conclusion:  Conclusion In this study an alternative approach was proposed to model calibration Results indicate that use of the Bayesian network for calibration is possible and is reasonable for the cases when the data is changing, whenever the model parameters have to be calibrated The parallel approach to populate the Bayesian network was shown using the SHARCNET Future Work :  Future Work Construct and populate the Bayesian network using more complicated hydrological models, such as SWAT Try calibrating parameters that are dependent on each other to better understand their dependence and behaivour Thank you!:  Thank you!

Related presentations


Other presentations created by Dabby

Propaganda Comparativa
16. 11. 2007
0 views

Propaganda Comparativa

ch 6 ppt
15. 06. 2007
0 views

ch 6 ppt

Feudal Japan Origin Religion
09. 10. 2007
0 views

Feudal Japan Origin Religion

Riedel DASER2
25. 09. 2007
0 views

Riedel DASER2

Shen CRF
25. 09. 2007
0 views

Shen CRF

Anna
11. 10. 2007
0 views

Anna

intro CS 3
16. 10. 2007
0 views

intro CS 3

TheatreHistoryO
17. 10. 2007
0 views

TheatreHistoryO

panama 5
22. 10. 2007
0 views

panama 5

Lesson 1 Intro and Pre WW II
22. 10. 2007
0 views

Lesson 1 Intro and Pre WW II

gf5
25. 09. 2007
0 views

gf5

hao discr prob mod rel dat
25. 09. 2007
0 views

hao discr prob mod rel dat

Correcting News Mistakes
05. 10. 2007
0 views

Correcting News Mistakes

MRCME Febrile Rash
23. 10. 2007
0 views

MRCME Febrile Rash

Microfinance MDGs
28. 11. 2007
0 views

Microfinance MDGs

kinetic models
25. 09. 2007
0 views

kinetic models

rtc
16. 10. 2007
0 views

rtc

debate
26. 10. 2007
0 views

debate

SALSA RTE Burchardt Frank
01. 11. 2007
0 views

SALSA RTE Burchardt Frank

Behav Interv Gay MA Users
02. 11. 2007
0 views

Behav Interv Gay MA Users

usits2001 talk
29. 10. 2007
0 views

usits2001 talk

ECCR IU Mar15 07
21. 11. 2007
0 views

ECCR IU Mar15 07

Lesson 1 Introduction
28. 12. 2007
0 views

Lesson 1 Introduction

99 ChemAware Chapter 03
02. 01. 2008
0 views

99 ChemAware Chapter 03

Dr G B Reddy
03. 01. 2008
0 views

Dr G B Reddy

ber
02. 08. 2007
0 views

ber

05 bandura
02. 08. 2007
0 views

05 bandura

Robins
25. 09. 2007
0 views

Robins

Comp Gen Phylo HMM
25. 09. 2007
0 views

Comp Gen Phylo HMM

plkongres2007 crop 04
04. 10. 2007
0 views

plkongres2007 crop 04

lysenko
26. 11. 2007
0 views

lysenko

CNE120 11 8 04
02. 08. 2007
0 views

CNE120 11 8 04

Martin Hilbert
22. 10. 2007
0 views

Martin Hilbert

antioxidants
04. 03. 2008
0 views

antioxidants

presentation reynolds
07. 11. 2007
0 views

presentation reynolds

certeau present
03. 01. 2008
0 views

certeau present

NewBrunswick
12. 03. 2008
0 views

NewBrunswick

JVM models in ACL2
25. 09. 2007
0 views

JVM models in ACL2

ge203 08
25. 03. 2008
0 views

ge203 08

Q307 englanti
26. 03. 2008
0 views

Q307 englanti

auerickson
25. 09. 2007
0 views

auerickson

EcologicalFootprints
07. 04. 2008
0 views

EcologicalFootprints

TradeinHealthService s130207
28. 03. 2008
0 views

TradeinHealthService s130207

april cyprus lnarayanan
30. 03. 2008
0 views

april cyprus lnarayanan

BRAMBLE
31. 12. 2007
0 views

BRAMBLE

Macro course 2005 lecture 4
09. 04. 2008
0 views

Macro course 2005 lecture 4

summit2008a
10. 04. 2008
0 views

summit2008a

Wayne NY NJPresentation
13. 04. 2008
0 views

Wayne NY NJPresentation

AE2 C04 2007
14. 04. 2008
0 views

AE2 C04 2007

Rinolfi
17. 10. 2007
0 views

Rinolfi

HDX4000 Training NA
22. 04. 2008
0 views

HDX4000 Training NA

chapman poster 14jan05
25. 09. 2007
0 views

chapman poster 14jan05

BBC Series State of the Earth
08. 10. 2007
0 views

BBC Series State of the Earth

1960spowerpoint
02. 11. 2007
0 views

1960spowerpoint

hansjeppson
15. 10. 2007
0 views

hansjeppson

hegel
05. 01. 2008
0 views

hegel

exec blue 060120
18. 06. 2007
0 views

exec blue 060120

Ethiopia session II
18. 06. 2007
0 views

Ethiopia session II

emergenuity
18. 06. 2007
0 views

emergenuity

experiencia aenor
18. 06. 2007
0 views

experiencia aenor

India Work Plan UNCT
07. 01. 2008
0 views

India Work Plan UNCT

Tropsha 4 5 05
24. 11. 2007
0 views

Tropsha 4 5 05

posterH2OinPFCs
01. 01. 2008
0 views

posterH2OinPFCs

etd2004
12. 10. 2007
0 views

etd2004

chi00
19. 11. 2007
0 views

chi00

38613SciTechStudies1
16. 10. 2007
0 views

38613SciTechStudies1

educause 2004 Fedora
25. 09. 2007
0 views

educause 2004 Fedora

cours7
23. 10. 2007
0 views

cours7

comics
15. 06. 2007
0 views

comics

Columbia Political Cartoons
15. 06. 2007
0 views

Columbia Political Cartoons

Collins Math Stats2
15. 06. 2007
0 views

Collins Math Stats2

Chapter Eight student version
15. 06. 2007
0 views

Chapter Eight student version

blagues
15. 06. 2007
0 views

blagues

Anime Manga Pres
15. 06. 2007
0 views

Anime Manga Pres

1193 Cartoons pig
15. 06. 2007
0 views

1193 Cartoons pig

1 cartoon
15. 06. 2007
0 views

1 cartoon

PBOCJapan060103
09. 10. 2007
0 views

PBOCJapan060103

control
15. 06. 2007
0 views

control

jcdl contentmodels
25. 09. 2007
0 views

jcdl contentmodels

curso dq abp joao
28. 12. 2007
0 views

curso dq abp joao

conf present 045
07. 01. 2008
0 views

conf present 045

05 International Conflict
23. 11. 2007
0 views

05 International Conflict

banse1
15. 06. 2007
0 views

banse1

Feg Express
18. 06. 2007
0 views

Feg Express

Fantasztikus programozas
18. 06. 2007
0 views

Fantasztikus programozas

smp99
25. 09. 2007
0 views

smp99

efg pr005
07. 11. 2007
0 views

efg pr005

F8 Femenino
18. 06. 2007
0 views

F8 Femenino

9 3 DEPAC SLPRS Ppresentation
29. 11. 2007
0 views

9 3 DEPAC SLPRS Ppresentation

geer sesiposter
25. 09. 2007
0 views

geer sesiposter