BN, BBN, CPN, ‘Bayesian Networks’

Why use BN
• To estimate quantities which are unobservable
              q
• Modelling ...
Example : Dryland Salinity WA   ………….[ land condition, forest changes]
SALINITY : Information Gap, Policy & Management Problem :
      – where is it, where changed, where will it.. MAP, MONITOR...
BIG AREA
~230,000 sq km
Knowledge about SALINITY PROCESS
– rising saline groundwater as the result of clearing
Sample ‘truth’
      Observational
      data – spatial
      Y/N (date?)




Knowledge about PROCESS




                ...
Network diagram – dryland salinity – FIRST VERSION




Is each location (likely to be) saline or not ? - Not observable di...
Network – dryland salinity – FIRST VERSION - getting the data




             processing                              cla...
Hydrologists
H d l i t concept di
                t diagram - NOT a BN




 Ground water
 depth and rate               Sal...
Water Poverty ‘Network’


              limitations for agriculture                    ?
              volume, critical su...
Land Monitor – Information Gap

•   The three highest priority environmental issues
    - Land salinisation,
    - Salinis...
Salinity Problem & Impact
Resource Problem affects people
Economic & Social Problem

Prediction 25% - 35% land lost

$$ - ...
Land Monitor - Components

I.
I Institutional support (agencies).
                        (agencies)

2. Demonstrated Tech...
CMIS Methods and technical developments

• Rectification & Registration, Calibration (robust regression)

• Discriminant A...
Salinity Mapping & Monitoring Ground Data
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Why use Bayesian Networks for poverty analysis

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Presented at the Basin Focal Project Poverty Mapping Workshop, November 2007, Chiang Mai, Thailand

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Why use Bayesian Networks for poverty analysis

  1. 1. BN, BBN, CPN, ‘Bayesian Networks’ Why use BN • To estimate quantities which are unobservable q • Modelling – building models, ‘elicitation’ • Mixed data, probabilistic relationships – medical diagnosis – inference, risk, decision support – estimation of physical state p y – ‘data integration’
  2. 2. Example : Dryland Salinity WA ………….[ land condition, forest changes]
  3. 3. SALINITY : Information Gap, Policy & Management Problem : – where is it, where changed, where will it.. MAP, MONITOR, PREDICT
  4. 4. BIG AREA ~230,000 sq km
  5. 5. Knowledge about SALINITY PROCESS – rising saline groundwater as the result of clearing
  6. 6. Sample ‘truth’ Observational data – spatial Y/N (date?) Knowledge about PROCESS Knowledge ?? Landscape position important - more likely in valleys Salinity affects vegetation - Visible effects ? – images Groundwater levels S il t G d t l l Soil type, Vegetation type, etc ???
  7. 7. Network diagram – dryland salinity – FIRST VERSION Is each location (likely to be) saline or not ? - Not observable directly Meaning of network, then - A. How do we observe (get data) on ‘Landform Position’ ? everywhere - B. How do we observe ‘vegetation condition’ ? (A from processing DEM; B [surrogate] classification from Landsat)
  8. 8. Network – dryland salinity – FIRST VERSION - getting the data processing classification task task DEM IMAGE ‘Raw data’ ?arrows? ‘Raw data’ ?what happens?
  9. 9. Hydrologists H d l i t concept di t diagram - NOT a BN Ground water depth and rate Salinity of rise ? Hydrological model - deterministic X Data Model Parameters
  10. 10. Water Poverty ‘Network’ limitations for agriculture ? volume, critical supply gap, uncertainty supply ?? Opportunity cost labour Poverty measure WP or surrogate g Water-related health costs Education/Invest ment constraints ?
  11. 11. Land Monitor – Information Gap • The three highest priority environmental issues - Land salinisation, - Salinisation of inland waters, and - Maintaining biodiversity (Western Australian State of the Environment Report, 1998) • About 1.8 million ha in WA are already salt-affected, and this area could double in the next 15 to 25 years years. • Effects on Vegetation • No Accurate map, No spatially explicit information on change, or prediction
  12. 12. Salinity Problem & Impact Resource Problem affects people Economic & Social Problem Prediction 25% - 35% land lost $$ - 40% Australia’s grain Farming is not subsidised in Aust g Business, Land value, Banks $ Built infrastucture : road network Maintenance; Town Buildings ‘Rescue Towns’
  13. 13. Land Monitor - Components I. I Institutional support (agencies). (agencies) 2. Demonstrated Technical Capacity (CMIS) Define necessary data (Landsat TM 1988-2000 DEM) 1988-2000, and methods 3. 3 Funding Support (National Govt) ------------------------ 4. Public Interest LANDSAT TM – Complete Australian Archive since 1988
  14. 14. CMIS Methods and technical developments • Rectification & Registration, Calibration (robust regression) • Discriminant Analysis (C (CVA etc) ) • Enhanced ML classification (PP – uncertainty) • DEM (pre)Processing – derived variables • Data Integration - CPN, Decision Trees • Trend summary and representation (vegetation condition) (others e.g. NN, LD, D i i T ( th NN LD Decision Trees …) )
  15. 15. Salinity Mapping & Monitoring Ground Data

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