Matches in SemOpenAlex for { <https://semopenalex.org/work/W608962888> ?p ?o ?g. }
- W608962888 endingPage "148" @default.
- W608962888 startingPage "134" @default.
- W608962888 abstract "Two corresponding issues concerning Digital Soil Mapping are the demand for up-to-date, fine resolution soil data and the need to determine soil–landscape relationships. In this study, we propose a Bayesian Network framework as a suitable modelling approach to fulfil these requirements. Bayesian Networks are graphical probabilistic models in which predictions are obtained using prior probabilities derived from either measured data or expert opinion. They represent cause and effect relationships through connections in a network system. The advantage of the Bayesian Networks approach is that the models are easy to interpret and the uncertainty inherent in the relationships between variables can be expressed in terms of probability. In this study we will define the fundamentals of a Bayesian Network and the probability theory that underpins predictions. Then, using case studies, we demonstrate how they can be applied to predict soil properties (bulk density) and soil taxonomic class (associations)." @default.
- W608962888 created "2016-06-24" @default.
- W608962888 creator A5015109819 @default.
- W608962888 creator A5022681636 @default.
- W608962888 creator A5046267849 @default.
- W608962888 creator A5049070638 @default.
- W608962888 creator A5059841891 @default.
- W608962888 creator A5070146278 @default.
- W608962888 creator A5080235603 @default.
- W608962888 date "2015-12-01" @default.
- W608962888 modified "2023-10-17" @default.
- W608962888 title "On the application of Bayesian Networks in Digital Soil Mapping" @default.
- W608962888 cites W1753166029 @default.
- W608962888 cites W1817561967 @default.
- W608962888 cites W1973107043 @default.
- W608962888 cites W1976452867 @default.
- W608962888 cites W1977210159 @default.
- W608962888 cites W1985554128 @default.
- W608962888 cites W1986443283 @default.
- W608962888 cites W1990797416 @default.
- W608962888 cites W1998811800 @default.
- W608962888 cites W2001391324 @default.
- W608962888 cites W2012469201 @default.
- W608962888 cites W2012565092 @default.
- W608962888 cites W2013725197 @default.
- W608962888 cites W2023939697 @default.
- W608962888 cites W2025566726 @default.
- W608962888 cites W2027891563 @default.
- W608962888 cites W2029072125 @default.
- W608962888 cites W2038606746 @default.
- W608962888 cites W2046139462 @default.
- W608962888 cites W2046359282 @default.
- W608962888 cites W2048076161 @default.
- W608962888 cites W2050191260 @default.
- W608962888 cites W2054325787 @default.
- W608962888 cites W2057165992 @default.
- W608962888 cites W2057688288 @default.
- W608962888 cites W2060710034 @default.
- W608962888 cites W2064831533 @default.
- W608962888 cites W2067125748 @default.
- W608962888 cites W2073018583 @default.
- W608962888 cites W2073103388 @default.
- W608962888 cites W2075634299 @default.
- W608962888 cites W2078474364 @default.
- W608962888 cites W2080952994 @default.
- W608962888 cites W2081340599 @default.
- W608962888 cites W2082708570 @default.
- W608962888 cites W2085593420 @default.
- W608962888 cites W2093939487 @default.
- W608962888 cites W2095071227 @default.
- W608962888 cites W2095235018 @default.
- W608962888 cites W2097155671 @default.
- W608962888 cites W2097508937 @default.
- W608962888 cites W2104896032 @default.
- W608962888 cites W2107945928 @default.
- W608962888 cites W2112411699 @default.
- W608962888 cites W2114836920 @default.
- W608962888 cites W2138240506 @default.
- W608962888 cites W2149160185 @default.
- W608962888 cites W2150798249 @default.
- W608962888 cites W2153944160 @default.
- W608962888 cites W3150110132 @default.
- W608962888 cites W4230900352 @default.
- W608962888 cites W4238795695 @default.
- W608962888 cites W4251368784 @default.
- W608962888 cites W79709819 @default.
- W608962888 doi "https://doi.org/10.1016/j.geoderma.2015.05.014" @default.
- W608962888 hasPublicationYear "2015" @default.
- W608962888 type Work @default.
- W608962888 sameAs 608962888 @default.
- W608962888 citedByCount "22" @default.
- W608962888 countsByYear W6089628882015 @default.
- W608962888 countsByYear W6089628882016 @default.
- W608962888 countsByYear W6089628882017 @default.
- W608962888 countsByYear W6089628882018 @default.
- W608962888 countsByYear W6089628882019 @default.
- W608962888 countsByYear W6089628882020 @default.
- W608962888 countsByYear W6089628882021 @default.
- W608962888 countsByYear W6089628882022 @default.
- W608962888 countsByYear W6089628882023 @default.
- W608962888 crossrefType "journal-article" @default.
- W608962888 hasAuthorship W608962888A5015109819 @default.
- W608962888 hasAuthorship W608962888A5022681636 @default.
- W608962888 hasAuthorship W608962888A5046267849 @default.
- W608962888 hasAuthorship W608962888A5049070638 @default.
- W608962888 hasAuthorship W608962888A5059841891 @default.
- W608962888 hasAuthorship W608962888A5070146278 @default.
- W608962888 hasAuthorship W608962888A5080235603 @default.
- W608962888 hasConcept C101112237 @default.
- W608962888 hasConcept C104471815 @default.
- W608962888 hasConcept C107673813 @default.
- W608962888 hasConcept C119857082 @default.
- W608962888 hasConcept C124101348 @default.
- W608962888 hasConcept C154945302 @default.
- W608962888 hasConcept C155846161 @default.
- W608962888 hasConcept C159390177 @default.
- W608962888 hasConcept C159750122 @default.
- W608962888 hasConcept C160234255 @default.