Paper Info
Title
A Bayesian approach to model dispersal for decision support.
Abstract
In agricultural and environmental sciences dispersal models are often used for risk assessment to predict the risk associated with a given configuration and also to test scenarios that are likely to minimise those risks. Like any biological process, dispersal is subject to biological, climatic and environmental variability and its prediction relies on models and parameter values which can only approximate the real processes. In this paper, we present a Bayesian method to model dispersal using spatial configuration and climatic data (distances between emitters and receptors; main wind direction) while accounting for uncertainty, with an application to the prediction of adventitious presence rate of genetically modified maize (GM) in a non-GM field. This method includes the design of candidate models, their calibration, selection and evaluation on an independent dataset. A group of models was identified that is sufficiently robust to be used for prediction purpose. The group of models allows to include local information and it reflects reliably enough the observed variability in the data so that probabilistic model predictions can be performed and used to quantify risk under different scenarios or derive optimal sampling schemes.
Year
DOI
Venue
2016
10.1016/j.envsoft.2015.12.018
Environmental Modelling & Software
Keywords
Field
DocType
Dispersal,Variability,Uncertainty,Bayesian inference,MCMC,Decision support,Risk assessment,Sampling,Zero-excess data
Data mining,Variable-order Bayesian network,Bayesian inference,Markov chain Monte Carlo,Computer science,Sampling (statistics),Statistical model,Probabilistic logic,Biological dispersal,Bayesian probability
Journal
Volume
Issue
ISSN
78
C
1364-8152
Citations 
PageRank 
References 
0
0.34
4
Authors
4
Name
Order
Citations
PageRank
Arnaud Bensadoun100.34
Hervé Monod231.58
David Makowski3293.52
Antoine Messéan401.01