BN, BBN, CPN, ‘Bayesian Networks’

Why use BN• To estimate quantities which are unobservableq• Modelling – building models, ‘elicitation’• Mixed data, probabilistic relationships

– medical diagnosis– inference, risk, decision support– estimation of physical statep 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 clearingrising saline groundwater as the result of clearing

Sample ‘truth’Observationaldata – spatial Y/N (date?)

Knowledge about PROCESS

Knowledge ??Landscape position importantLandscape position important- more likely in valleys

Salinity affects vegetation- Visible effects ? – images

G d t l l S il tGroundwater levels 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

DEM IMAGE

processing task

classification task

DEM

‘Raw data’

IMAGE

‘Raw data’?arrows??what happens?

H d l i t t di NOT BNHydrologists concept diagram - NOT a BN

SalinityGround water depth and rate of rise

?

XHydrological model- deterministic Data

Model Parameters

XModel Parameters

Water Poverty ‘Network’

limitations for agriculturevolume, critical supply gap,uncertainty supply

?

uncertainty supply

Opportunity

??

WPPoverty measureor surrogate

Opportunity cost labour

g

Water-related health costs

?

Education/Investment constraints

Land Monitor – Information Gap

• The three highest priority environmental issues- Land salinisation, - Salinisation of inland waters, and

Maintaining biodiversity- 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 yearsand this area could double in the next 15 to 25 years.

• Effects on Vegetation

• No Accurate map, No spatially explicit information on change, or prediction

Salinity Problem & ImpactResource Problem affects peopleEconomic & Social ProblemEconomic & Social Problem

Prediction 25% - 35% land lost

$$ - 40% Australia’s grain

Farming is not subsidised in AustgBusiness, Land value, Banks $

Built infrastucture : road networkMaintenance;Town Buildings ‘Rescue Towns’

Land Monitor ComponentsLand Monitor - Components

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

2. Demonstrated Technical Capacity (CMIS) Define necessary data (Landsat TM 1988-2000 DEM)Define necessary data (Landsat TM 1988-2000, DEM)and methods

3 Funding Support (National Govt)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)

(C )• Discriminant Analysis (CVA etc)

• Enhanced ML classification (PP – uncertainty)

• DEM (pre)Processing – derived variables

• Data Integration - CPN, Decision Trees

• Trend summary and representation (vegetation condition)

( th NN LD D i i T )(others e.g. NN, LD, Decision Trees …)

Salinity Mapping & Monitoring Ground DataSalinity Mapping & Monitoring Ground Data