Bayesian Networks for Environmental Resource Management

1. Bayesian NetworksforEnvironmental Resource Management Peter Towbin Applied Math and Statistics

2. 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!

3. Why are Bayesian Networks of interest? • Knowledge discovery: surveys. • Inference: decision support: water treatment. • Group cognition, trust.

4. 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.

5. MRC Decision Support System

6. 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

7. Community Forestry Management: Village Survey Team

13. 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.

19. 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

20. 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.

21. 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.

22. 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.

23. MCMC: Metropolis-Hastings

24. Run Times

25. Variances

26. The End