Aller au contenu principal

page search

Bibliothèque River water quality assessment: A comparison of binary- and fuzzy logic-based approaches

River water quality assessment: A comparison of binary- and fuzzy logic-based approaches

River water quality assessment: A comparison of binary- and fuzzy logic-based approaches

Resource information

Date of publication
Décembre 2012
Resource Language
ISBN / Resource ID
AGRIS:US201500219332
Pages
132-140

European Union defined strategies for surface water quality with the 2000/60/EC, or Water Framework Directive (WFD), in order to safeguard Union's water environment; therefore policies have been implemented and became part of Member States legislations. WFD sets guidelines regarding control of river water quality as well as land use planning. However, there is a real requirement of practical investigating procedures, as well as water quality management tools, to help professionals to properly assess the water quality status. In addition, sampling frequencies can be optimized in order to reduce their costs. One of the possible approaches is substantiated into the fuzzy logic, which allows to consider variables not in a binary code but with a certain degree of membership for each of the described classes. The aim of the present study is to show how fuzzy analysis can be implemented when running a water quality assessment, and then focus on a relevant case study. Fuzzy logic approach is studied in order to manage the subjectivity of the analysis, in which examined indicators are classified after data fuzzification and a subsequent defuzzification. A potential optimization of water quality assessment would then reduce sampling frequencies when downward or steady-state trends are found during monitoring campaigns. Sampling frequency studies and non-parametric tests have also been proposed as possible water quality management tools for the application of the EU directive.

Share on RLBI navigator
NO

Authors and Publishers

Author(s), editor(s), contributor(s)

Scannapieco, D.
Naddeo, V.
Zarra, T.
Belgiorno, V.

Publisher(s)
Data Provider