Bayesian network (BN) modeling is a rapidly advancing field. Here we explore new methods
by which BN model development and application are being joined with other tools and …

Abstract Dynamic Bayesian networks (DBNs) as an extension of traditional Bayesian
networks have recently been paid great concern to environmental modeling to capture …

An integrated approach combining Bayesian Network with GIS was developed for making a
probabilistic prediction of sea level rise induced coastal erosion and assessing the …

Abstract Bayesian Networks (BNs) are commonly used to model socio-ecological systems,
as their graphical structure supports participatory modelling, they can integrate quantitative …

Spatial interactions underlying consecutive sequential snapshots of spatial distributions,
such as the migration flows underlying temporal population snapshots, can reflect the details …

The continuous, intense exploitation of resources under rapid industrialization has made the
ecological environment of resource-based cities increasingly vulnerability. Negative impacts …

In many complex, real‐world situations, problem solving and decision making require
effective reasoning about causation and uncertainty. However, human reasoning in these …

Numerous studies attempt to unravel the role played by Biodiversity in ecosystems and ES
reliance on Biodiversity. Achieving this aim is difficult given: the multi-layered Biodiversity …

Abstract Nine Bayesian Belief Networks (BBNs) were developed within the OpenNESS
project specifically for modelling ecosystem services for case study applications. The novelty …

Prevention of ecological risks in land ecosystems is crucial for environmental protection and
sustainable land use. With increasingly severe land degradation, new and effective methods …