Reforestation planning using Bayesian networks. Ordóñez Galán, C., Matías, J., Rivas, T., & Bastante, F. Environmental Modelling & Software, 24(11):1285-1292, 11, 2009. ![Reforestation planning using Bayesian networks [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex The aim of this research was to construct a reforestation model for woodland located in the basin of the river Liébana (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.
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abstract = {The aim of this research was to construct a reforestation model for woodland located in the basin of the river Liébana (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.},
bibtype = {article},
author = {Ordóñez Galán, C. and Matías, J.M. and Rivas, T. and Bastante, F.G.},
doi = {10.1016/j.envsoft.2009.05.009},
journal = {Environmental Modelling & Software},
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