The application of expert knowledge in Bayesian networks to predict soil bulk density at the landscape scale

K. Taalab, R. Corstanje*, T.M. Mayr, M.J. Whelan, R.E. Creamer

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)

Abstract

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 (Db) 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 Db 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 Db 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.

Original languageEnglish
Pages (from-to)930-941
Number of pages12
JournalEuropean Journal of Soil Science
Volume66
Issue number5
DOIs
Publication statusPublished - 1 Sep 2015
Externally publishedYes

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