Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fractal cities: a geometry of form and function
Fractal cities: a geometry of form and function
Real-world applications of Bayesian networks
Communications of the ACM
A tutorial on learning with Bayesian networks
Learning in graphical models
Graphical Models: Foundations of Neural Computation
Graphical Models: Foundations of Neural Computation
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Application of Bayesian Network Learning Methods to Waste Water Treatment Plants
Applied Intelligence
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Learning Bayesian Networks
Operations for learning with graphical models
Journal of Artificial Intelligence Research
Integrating spatial relations into case-based reasoning to solve geographic problems
Knowledge-Based Systems
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Complex systems theory and Cellular Automata (CA) are widely used in geospatial modeling. However, existing models have been limited by challenges such as handling of multiple datasets, parameter definition and the calibration procedures in the modeling process. Bayesian network (BN) formalisms provide an alternative method to address the drawbacks of these existing models. This study proposes a hybrid model that integrates BNs, CA and Geographic Information Systems (GIS) to model land use change. The transition rules of the CA model are generated from a graphical formalism where the key land use drivers are represented by nodes and the dependencies between them are expressed by conditional probabilities extracted from historical spatial datasets. The results indicate that the proposed model is able to realistically simulate and forecast spatio-temporal process of land use change. Further, it forms the basis for new synergies in CA model design that can lead to improved model outcomes.