Application of Bayesian Belief Networks to quantify and map areas at risk to soil threats: Using soil compaction as an example

Soil “Ecosystem” Services and Natural Capital: Critical Appraisal of Research on Uncertain Ground

“…Since their introduction in the mid-80s, BBNs have been adopted in various scientific fields, and are becoming increasingly popular tools in environmental modeling (e.g., Sadoddin et al, 2009;Haines-Young, 2011;Farmani et al, 2012;Henriksen et al, 2012;Troldborg et al, 2013), in particular to quantify and map ecosystem services (Grêt-Regamey et al, 2013;Landuyt et al, 2013Landuyt et al, , 2015Celio et al, 2014;Rositano and Ferraro, 2014;Taalab et al, 2015). Formally, a BBN is a probabilistic model that represents graphically a set of random variables and their conditional dependencies via a network, referred to technically as a directed acyclic graph (DAG).…”

A Multiply Connected Belief Network approach for schedule risk analysis of metropolitan construction projects

“…A BN is essentially a representation of the joint probability distribution of the entire domain of system variables (Troldborg et al 2013). The joint distribution of a network is obtained through the chain rule as follow: let BN be a network over U = {A 1 , … , A n }.…”

Risk analysis of emergent water pollution accidents based on a Bayesian Network

“…It can describe events in different states, effectively use given information to deduce unknown information, and has very strong computational reasoning ability (Pearl, 1986). BN model is widely used in emergency risk analysis of maritime collisions (Trucco et al, 2008); genetic resources, water regulation, recreation, freshwater provision, and climate regulation in ecosystem services (Landuyt et al, 2013); estimating the risk of soil compaction (Troldborg et al, 2013); evaluating the influential factors in traffic accidents; and predicting probability of accident (Zhang, 2013). The characteristics of drivers, highways, trucks, accidents, and atmospheric factors are contributing factors in traffic accidents on rural highways in Spain (de Oña et al, 2013).…”