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’

Example : Dryland Salinity WA ………….[ land condition, forest changes]

SALINITY : Information Gap, Policy & Management Problem :
– where is it, where changed, where will it.. MAP, MONITOR, PREDICT

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
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 ???

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)

Network – dryland salinity – FIRST VERSION - getting the data
processing classification task
task
DEM IMAGE
‘Raw data’ ?arrows? ‘Raw data’
?what happens?

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

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
?

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

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’

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

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 …)
)

Salinity Mapping & Monitoring Ground Data