Bayesian Networksfor

Environmental Resource Management

Peter TowbinApplied Math and Statistics

Bayesian Networksfor

Environmental Resource Management

1. Context: Why are Bayesian Networks of interest?

2. Goal: Assess BN research tools available for ERM.

3. Project: Exploring Open Source alpha releases!

Why are Bayesian Networks of interest?

• Knowledge discovery: surveys.

• Inference: decision support: water treatment.

• Group cognition, trust.

Mekong River CommissionMandate For Public Participation

Involvement of public and the public opinion in the workof MRC is believed to be a prerequisite for the overall aimand vision of our Mekong Agreement, i.e., sustainabledevelopment of the Mekong River Basin. As a case in point,public inputs are expected to be required at the various stages of the formulation of the Basin Development Plan.

Public Participation is a process through which key stakeholders gain influence and take part in decision making in the planning, implementation, monitoring and evaluation of MRC programs and projects.

“Public Participation in the Context of the MRC”- Approved by MRC Joint Committee, 1999.

MRC Decision Support System

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One of the most direct intersections of decision support technology and participatory resource management has been in the field of PPGIS: Public Participatory Geographic Information Systems

PPGIS systems are being used to manage problems such as erosion, deforestation, and over-fishing by documenting land rights and providing context and focus for decision making and management.

Mt. Pulag National Park Benguet, Nueva Vizcaya and Ifugao villages, Philippines.

Scale: 1:10,000, Area covered: 360 km 2.

Manual on Participatory 3-Dimensional Modeling for Natural Resource Management By Giacomo Rambaldi and Jasmin Callosa-Tarr

PPGIS: Public Participatory GIS

Community Forestry Management: Village Survey Team

Group Model Building

• Stimulate knowledge elicitation/discovery.– “Tacit Knowledge”

• Better decision compliance, because:– Sense of ownership of the process.

– Model captures participant requirements.

– Model facilitates ongoing dialog and learning.

Choosing a Bayes Net Package

• Compatibility with GIS package: GeoNetworks Open Source java GIS. (Although NASA World Wind hails!)

• Source access required: “Open Source”.• Portability, existing graphics capability.

• unbbayes.sourceforge.net

• jbnc.sourceforge.net

• bnj.sourceforge.net

Assessing BNJ

• ~200 files of code. GUI• alpha release of 3rd Gen: Problem.• Not much in the way of learning yet (port K2)• Exact and approximate inference algorithms:

– Graph/clique algorithms.– Pearl, Variable Elimination, Message Passing.– PolyTree Reduction, Edge Deletion.

• What if some nodes are not tabular (spatio-temporal model…): Sampling.

Sampling Algorithms

I implemented two algorithms using BNJ data structures andnetwork import utilities:

• Forward sampling.• (Gibbs sampling).• Metropolis Hastings MCMC sampling.

Used one of their algorithms to compare and check results:

• AIS: Adaptive Importance Sampling.

Forward Sampling

• Simple and elegant.

• Topological ordering.

• Sample at each node: p(node | Parents).

• Issue: given unlikely evidence in the graph, may have large percentage of samples fail.

MCMC: Metropolis-Hastings

Run Times

Variances

The End