Reforestation planning using Bayesian networks

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.

[1]  Sunil Narumalani,et al.  Aquatic macrophyte modeling using GIS and logistic multiple regression , 1997 .

[2]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[3]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[4]  B. Marcot,et al.  Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation , 2006 .

[5]  Steen Andreassen,et al.  Medical expert systems based on causal probabilistic networks , 1991 .

[6]  Ángel M. Felicísimo,et al.  A comparison between some statistical methods commonly used in predictive modeling , 2004 .

[7]  David J. Spiegelhalter,et al.  Local computations with probabilities on graphical structures and their application to expert systems , 1990 .

[8]  Richard E. Neapolitan,et al.  Learning Bayesian networks , 2007, KDD '07.

[9]  Dragan A. Savic,et al.  An evolutionary Bayesian belief network methodology for optimum management of groundwater contamination , 2009, Environ. Model. Softw..

[10]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[11]  Wray L. Buntine.  A Guide to the Literature on Learning Probabilistic Networks from Data , 1996, IEEE Trans. Knowl. Data Eng..

[12]  Jouko Lampinen,et al.  Bayesian neural networks for industrial applications , 1999, SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269).

[13]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[14]  Celestino Ordóñez,et al.  Learning Machines Applied to Potential Forest Distribution , 2005, Environmental management.

[15]  Alfonso Dominguez,et al.  Bayesian networks in planning a large aquifer in Eastern Mancha, Spain , 2007, Environ. Model. Softw..

[16]  P. Spirtes,et al.  Causation, prediction, and search , 1993 .

[17]  Xiaohui Liu,et al.  A spatio-temporal Bayesian network classifier for understanding visual field deterioration , 2005, Artif. Intell. Medicine.

[18]  Cornelis W. P. M. Blom,et al.  Vegetation zonation in a former tidal area: A vegetation-type response model based on DCA and logistic regression using GIS , 1996 .

[19]  Mohammad Reza Nikoo,et al.  Developing real time operating rules for trading discharge permits in rivers: Application of Bayesian Networks , 2009, Environ. Model. Softw..

[20]  David J. Spiegelhalter,et al.  Probabilistic Networks and Expert Systems , 1999, Information Science and Statistics.

[21]  Josef Kittler,et al.  Application of a Bayesian Network in a GIS Based Decision Making System , 1998, Int. J. Geogr. Inf. Sci..

[22]  Kevin B. Korb,et al.  Bayesian Artificial Intelligence , 2004, Computer science and data analysis series.

[23]  Jesús Muñoz,et al.  Comparison of statistical methods commonly used in predictive modelling , 2004 .

[24]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .

[25]  Judea Pearl,et al.  Fusion, Propagation, and Structuring in Belief Networks , 1986, Artif. Intell..

[26]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.

[27]  Ángel M. Felicísimo,et al.  Modeling the Potential Distribution of Forests with a GIS , 2002 .

[28]  Andrea Castelletti,et al.  Bayesian Networks and participatory modelling in water resource management , 2007, Environ. Model. Softw..

[29]  Andrew K. Skidmore,et al.  Unsupervised training area selection in forests using a nonparametric distance measure and spatial information , 1989 .

[30]  Yue Ouyang,et al.  Bayesian Multi‐net Classifier for classification of remote sensing data , 2006 .