2019-08-26T02:10:00Z
https://digital.csic.es/dspace-oai/request
oai:digital.csic.es:10261/27505
2016-02-16T08:37:44Z
com_10261_5062
com_10261_5
col_10261_5064
Darbra, Rosa María
Eljarrat, Ethel
Barceló, Damià
2010-09-08T13:12:38Z
2010-09-08T13:12:38Z
2008-04-01
TRAC - Trends in Analytical Chemistry
0165-9936 (Print)
http://hdl.handle.net/10261/27505
10.1016/j.trac.2008.02.005
Environmental risk assessment is an essential element in any decision-making
process in order to minimize the effects of human activities on the environment.
Unfortunately, often environmental data tends to be vague and imprecise, so uncertainty is associated with any study related with these kind of data.
Essentially, uncertainty in risk assessment may have two origins – randomness and incompleteness. There are two main ways to deal with these uncertainties – probability theory and fuzzy logic.
Probability theory is based on a stochastic approach, using probability functions to describe random variability in environmental parameters.
Fuzzy logic uses membership functions and linguistic parameters to express vagueness in environmental issues.
We discuss the best way to deal with uncertainties in the environmental field and give examples of probabilistic and fuzzy-logic approaches applied to environmental risk assessment.
eng
openAccess
Environmental risk assessment
Fuzzy logic
Monte Carlo
Probability theory
Uncertainty
How to measure uncertainties in environmental risk assassment
Artículo