Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Bayesian Networks and participatory modelling in water resource management
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
A spatio-temporal Bayesian network classifier for understanding visual field deterioration
Artificial Intelligence in Medicine
Environmental Modelling & Software
Modeling net ecosystem metabolism with an artificial neural network and Bayesian belief network
Environmental Modelling & Software
Review: Bayesian networks in environmental modelling
Environmental Modelling & Software
Good practice in Bayesian network modelling
Environmental Modelling & Software
Environmental Modelling & Software
Bayesian belief modeling of climate change impacts for informing regional adaptation options
Environmental Modelling & Software
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The aim of this research was to construct a reforestation model for woodland located in the basin of the river Liebana (NW Spain). This is essentially a pattern recognition problem: the class labels are types of woodland, and the variables for each point are environmental coordinates (referring to altitude, slope, rainfall, lithology, etc.). The model trained using data for existing wooded areas will serve as a guideline for the reforestation of deforested areas. Nonetheless, with a view to tackling reforestation from a more informed perspective, of interest is an interpretable model of relationships existing not just between woodland type and environmental variables but also between and among the environmental variables themselves. For this reason we used Bayesian networks, as a tool that is capable of constructing a causal model of the relationships existing between all the variables represented in the model. The prediction results obtained were compared with those for classical linear techniques, neural networks and support vector machines.