Journal article

Modelling spatial and temporal changes with GIS and Spatial and Dynamic Bayesian Networks

Yung En Chee, Lauchlin Wilkinson, Ann E Nicholson, Pedro F Quintana-Ascencio, John E Fauth, Dianne Hall, Kimberli J Ponzio, Libby Rumpff



State-and-transition models (STMs) have been successfully combined with Dynamic Bayesian Networks (DBNs) to model temporal changes in managed ecosystems. Such models are useful for exploring when and how to intervene to achieve the desired management outcomes. However, knowing where to intervene is often equally critical. We describe an approach to extend state-and-transition dynamic Bayesian networks (ST-DBNs) - incorporating spatial context via GIS data and explicitly modelling spatial processes using spatial Bayesian networks (SBNs). Our approach uses object-oriented (OO) concepts and exploits the fact that ecological systems are hierarchically structured. This allows key phenomena and ec..

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Awarded by ARC

Funding Acknowledgements

This work was supported by ARC Linkage Projects LP110100304 and LP110100321, the Australian Centre of Excellence for Risk Analysis, and the ARC Centre of Excellence for Environmental Decisions. Steven R Miller, Jo Anna Emanuel, Ken Snyder, Chris Oman provided critical information about the St Johns River catchment, and knowledge about willow ecology and management. We thank Steve Sinclair for assistance with developing the woodlands weed model, and Owen Woodberry for valuable discussions about approximation errors and stochastic simulation for approximate inference.