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The application of expert knowledge in Bayesian networks to predict soil bulk density at the landscape scale
Author(s) -
Taalab K.,
Corstanje R.,
Mayr T. M.,
Whelan M. J.,
Creamer R. E.
Publication year - 2015
Publication title -
european journal of soil science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.244
H-Index - 111
eISSN - 1365-2389
pISSN - 1351-0754
DOI - 10.1111/ejss.12282
Subject(s) - topsoil , digital soil mapping , scale (ratio) , soil map , bayesian network , computer science , bayesian probability , random forest , benchmark (surveying) , data mining , environmental science , machine learning , soil science , artificial intelligence , cartography , geography , soil water
Summary This paper investigates the use of expert knowledge as a resource for digital soil mapping. To do this, three models of topsoil soil bulk density ( D b ) were produced: (i) a random forest model formulated and cross‐validated with the limited data available (which served as the benchmark), (ii) a naïve Bayesian network ( BN ) where the conditional probabilities that define the relations between D b and explanatory landscape variables were derived from expert knowledge rather than data and (iii) a ‘hierarchical’ BN where model structure was also defined by expert knowledge. These models were used to generate spatial predictions for mapping topsoil D b at a landscape scale. The results show that expert knowledge‐based models can identify the same spatial trends in soil properties at a landscape scale as state‐of‐the‐art mapping algorithms. This means that they are a viable option for soil mapping applications in areas that have limited empirical data.