Bayesian Networks and participatory modelling in water resource management
Environmental Modelling & Software
Public participation modelling using Bayesian networks in management of groundwater contamination
Environmental Modelling & Software
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Causality: Models, Reasoning and Inference
Causality: Models, Reasoning and Inference
Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment
International Journal of Remote Sensing
Good practice in Bayesian network modelling
Environmental Modelling & Software
| Hi-index | 0.00 |
Land use decisions result from complex deliberative processes and fundamentally influence the livelihoods of many. These decisions are made based on quantitatively measurable information like topography and on qualitative criteria such as personal preferences. Bayesian networks (BN) are able to integrate both quantitative and qualitative data and are thus suitable to approach such processes. We model land use decisions in a pre-Alpine area in Switzerland, integrating biophysical data and local actors' knowledge into a spatially explicit BN. A structured experts' process to elaborate three different BN including agriculture, forestry, and settlement provides the base for the modeling. A spatially explicit updating of the BN via questionnaires enables us to take local actors' characteristics into account. Results show which drivers are most important for land use decision-making in our case study region, and how an alteration of these drivers could change future land use. Furthermore, focusing on the probability of occurrence of various land uses in a spatially explicit manner gives insights into path-dependency of land use change. This knowledge can serve as information for planners and policy makers to design more effective policy instruments.