Using classification trees techniques like sensitivity analysis in the field of radioecology
Titre du congrès :Fifth International Conference on
Sensitivity Analysis of Model Output
Ville du congrès :Budapest
Date du congrès :18/06/2007
This study is realized in the framework of the SENSIB project (an acronym referring to radioecological sensitivity) (Mercat-Rommens and Renaud ) which has been developed since 2003 by the French Institute for Radioprotection and Nuclear Safety (IRSN) and benefits from the financial support of ADEME, French Environment and Energy Management Agency. The main goal is to develop a standardised tool with a single scale of indexes to describe and compare the sensitivity of various environments to radioactive pollutions. Each index will represent a level of response of an environment to a pollution; for example an index of 1 refers to a low sensitive territory whereas a level of 5 refers to a high sensitive territory.
This communication focuses solely on the agricultural aspects of the SENSIB project. The objective is to determine whose factors (agronomical or radioecological) are of prime influence on the radioactive contamination of agricultural productions and will be the bases for the indexes construction. The identification of characteristics of the French territories whose stronger influence the fate of a radioactive contamination in the environment is based on radioecological models. These models are generally nonlinear and utilize agronomical and radioecological input variables, often linked by linear and/or non-linear relations. That is why in order to obtain more knowledge and precision of how the models work, we decided to perform an original global sensitivity analysis by using classification trees techniques (Mishra and al ). Contrary to the other methods of global sensitivity analysis (Saltelli et al ), the classification trees techniques allow to determine which input variables or associations of input variables contribute mainly to the different categories (predetermined) of the model output. So, the pathways linking the input variables and the output of the model can be more precisely described and used to propose recommendations to mitigate the consequences of environmental radioactive contamination.