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Bayesian Networks for Environmental Resource Management. Peter Towbin Applied Math and Statistics. Bayesian Networks for Environmental Resource Management. Context: Why are Bayesian Networks of interest? Goal: Assess BN research tools available for ERM.

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Bayesian networks for environmental resource management
Bayesian NetworksforEnvironmental Resource Management

Peter Towbin

Applied Math and Statistics

Bayesian networks for environmental resource management1
Bayesian NetworksforEnvironmental Resource Management

  • Context: Why are Bayesian Networks of interest?

  • Goal: Assess BN research tools available for ERM.

  • Project: Exploring Open Source alpha releases!

Why are bayesian networks of interest
Why are Bayesian Networks of interest?

  • Knowledge discovery: surveys.

  • Inference: decision support: water treatment.

  • Group cognition, trust.

Mekong river commission mandate for public participation
Mekong River CommissionMandate For Public Participation

  • Involvement of public and the public opinion in the work

  • of MRC is believed to be a prerequisite for the overall aim

  • and vision of our Mekong Agreement, i.e., sustainable

  • development 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.

Ppgis public participatory gis
PPGIS: Public Participatory GIS

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

Group model building
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
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.




Assessing bnj
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
Sampling Algorithms

  • I implemented two algorithms using BNJ data structures and

  • network 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
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.